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README.md

@@ -1,3 +1,110 @@
----
-license: apache-2.0
----
+# MotionBERT
+
+<a href="https://pytorch.org/get-started/locally/"><img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch&logoColor=white"></a> [![arXiv](https://img.shields.io/badge/arXiv-2210.06551-b31b1b.svg)](https://arxiv.org/abs/2210.06551) <a href="https://motionbert.github.io/"><img alt="Project" src="https://img.shields.io/badge/-Project%20Page-lightgrey?logo=Google%20Chrome&color=informational&logoColor=white"></a> <a href="https://youtu.be/slSPQ9hNLjM"><img alt="Demo" src="https://img.shields.io/badge/-Demo-ea3323?logo=youtube"></a> 
+
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/motionbert-unified-pretraining-for-human/monocular-3d-human-pose-estimation-on-human3)](https://paperswithcode.com/sota/monocular-3d-human-pose-estimation-on-human3?p=motionbert-unified-pretraining-for-human)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/motionbert-unified-pretraining-for-human/one-shot-3d-action-recognition-on-ntu-rgbd)](https://paperswithcode.com/sota/one-shot-3d-action-recognition-on-ntu-rgbd?p=motionbert-unified-pretraining-for-human)
+[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/motionbert-unified-pretraining-for-human/3d-human-pose-estimation-on-3dpw)](https://paperswithcode.com/sota/3d-human-pose-estimation-on-3dpw?p=motionbert-unified-pretraining-for-human)
+
+This is the official PyTorch implementation of the paper *"[Learning Human Motion Representations: A Unified Perspective](https://arxiv.org/pdf/2210.06551.pdf)"*.
+
+<img src="https://motionbert.github.io/assets/teaser.gif" alt="" style="zoom: 60%;" />
+
+## Installation
+
+```bash
+conda create -n motionbert python=3.7 anaconda
+conda activate motionbert
+# Please install PyTorch according to your CUDA version.
+conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
+pip install -r requirements.txt
+```
+
+
+
+## Getting Started
+
+| Task                              | Document                                                     |
+| --------------------------------- | ------------------------------------------------------------ |
+| Pretrain                          | [docs/pretrain.md](docs/pretrain.md)                                                          |
+| 3D human pose estimation          | [docs/pose3d.md](docs/pose3d.md) |
+| Skeleton-based action recognition | [docs/action.md](docs/action.md) |
+| Mesh recovery                     | [docs/mesh.md](docs/mesh.md) |
+
+
+
+## Applications
+
+### In-the-wild inference (for custom videos)
+
+Please refer to [docs/inference.md](docs/inference.md).
+
+### Using MotionBERT for *human-centric* video representations
+
+```python
+'''	    
+  x: 2D skeletons 
+    type = <class 'torch.Tensor'>
+    shape = [batch size * frames * joints(17) * channels(3)]
+    
+  MotionBERT: pretrained human motion encoder
+    type = <class 'lib.model.DSTformer.DSTformer'>
+    
+  E: encoded motion representation
+    type = <class 'torch.Tensor'>
+    shape = [batch size * frames * joints(17) * channels(512)]
+'''
+E = MotionBERT.get_representation(x)
+```
+
+
+
+> **Hints**
+>
+> 1. The model could handle different input lengths (no more than 243 frames). No need to explicitly specify the input length elsewhere.
+> 2. The model uses 17 body keypoints ([H36M format](https://github.com/JimmySuen/integral-human-pose/blob/master/pytorch_projects/common_pytorch/dataset/hm36.py#L32)). If you are using other formats, please convert them before feeding to MotionBERT. 
+> 3. Please refer to [model_action.py](lib/model/model_action.py) and [model_mesh.py](lib/model/model_mesh.py) for examples of (easily) adapting MotionBERT to different downstream tasks.
+> 4. For RGB videos, you need to extract 2D poses ([inference.md](docs/inference.md)), convert the keypoint format ([dataset_wild.py](lib/data/dataset_wild.py)), and then feed to MotionBERT ([infer_wild.py](infer_wild.py)).
+>
+
+
+
+## Model Zoo
+
+<img src="https://motionbert.github.io/assets/demo.gif" alt="" style="zoom: 50%;" />
+
+| Model                           | Download Link                                                | Config                                                       | Performance      |
+| ------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ---------------- |
+| MotionBERT (162MB)              | [OneDrive](https://1drv.ms/f/s!AvAdh0LSjEOlgS425shtVi9e5reN?e=6UeBa2) | [pretrain/MB_pretrain.yaml](configs/pretrain/MB_pretrain.yaml) | -                |
+| MotionBERT-Lite (61MB)          | [OneDrive](https://1drv.ms/f/s!AvAdh0LSjEOlgS27Ydcbpxlkl0ng?e=rq2Btn) | [pretrain/MB_lite.yaml](configs/pretrain/MB_lite.yaml)       | -                |
+| 3D Pose (H36M-SH, scratch)      | [OneDrive](https://1drv.ms/f/s!AvAdh0LSjEOlgSvNejMQ0OHxMGZC?e=KcwBk1) | [pose3d/MB_train_h36m.yaml](configs/pose3d/MB_train_h36m.yaml) | 39.2mm (MPJPE)   |
+| 3D Pose (H36M-SH, ft)           | [OneDrive](https://1drv.ms/f/s!AvAdh0LSjEOlgSoTqtyR5Zsgi8_Z?e=rn4VJf) | [pose3d/MB_ft_h36m.yaml](configs/pose3d/MB_ft_h36m.yaml)     | 37.2mm (MPJPE)   |
+| Action Recognition (x-sub, ft)  | [OneDrive](https://1drv.ms/f/s!AvAdh0LSjEOlgTX23yT_NO7RiZz-?e=nX6w2j) | [action/MB_ft_NTU60_xsub.yaml](configs/action/MB_ft_NTU60_xsub.yaml) | 97.2% (Top1 Acc) |
+| Action Recognition (x-view, ft) | [OneDrive](https://1drv.ms/f/s!AvAdh0LSjEOlgTaNiXw2Nal-g37M?e=lSkE4T) | [action/MB_ft_NTU60_xview.yaml](configs/action/MB_ft_NTU60_xview.yaml) | 93.0% (Top1 Acc) |
+| Mesh (with 3DPW, ft)            | [OneDrive](https://1drv.ms/f/s!AvAdh0LSjEOlgTmgYNslCDWMNQi9?e=WjcB1F) | [mesh/MB_ft_pw3d.yaml](configs/mesh/MB_ft_pw3d.yaml)              | 88.1mm (MPVE)    |
+
+In most use cases (especially with finetuning), `MotionBERT-Lite` gives a similar performance with lower computation overhead. 
+
+
+
+## TODO
+
+- [x] Scripts and docs for pretraining
+
+- [x] Demo for custom videos
+
+
+
+## Citation
+
+If you find our work useful for your project, please consider citing the paper:
+
+```bibtex
+@article{motionbert2022,
+  title   =   {Learning Human Motion Representations: A Unified Perspective}, 
+  author  =   {Zhu, Wentao and Ma, Xiaoxuan and Liu, Zhaoyang and Liu, Libin and Wu, Wayne and Wang, Yizhou},
+  year    =   {2022},
+  journal =   {arXiv preprint arXiv:2210.06551},
+}
+```
+

configs/action/MB_ft_NTU120_oneshot.yaml

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+# General  
+finetune: True
+partial_train: null
+
+# Traning 
+n_views: 2
+temp: 0.1
+
+epochs: 50
+batch_size: 32 
+lr_backbone: 0.0001
+lr_head: 0.001
+weight_decay: 0.01
+lr_decay: 0.99
+
+# Model
+model_version: embed
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+num_joints: 17
+hidden_dim: 2048
+dropout_ratio: 0.1
+
+# Data
+clip_len: 100
+
+# Augmentation
+random_move: True
+scale_range_train: [1, 3]
+scale_range_test: [2, 2]

configs/action/MB_ft_NTU60_xsub.yaml

@@ -0,0 +1,35 @@
+# General  
+finetune: True
+partial_train: null
+
+# Traning 
+epochs: 300
+batch_size: 32 
+lr_backbone: 0.0001
+lr_head: 0.001
+weight_decay: 0.01
+lr_decay: 0.99
+
+# Model
+model_version: class
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+num_joints: 17
+hidden_dim: 2048
+dropout_ratio: 0.5
+
+# Data
+dataset: ntu60_hrnet
+data_split: xsub
+clip_len: 243
+action_classes: 60
+
+# Augmentation
+random_move: True
+scale_range_train: [1, 3]
+scale_range_test: [2, 2]

configs/action/MB_ft_NTU60_xview.yaml

@@ -0,0 +1,35 @@
+# General  
+finetune: True
+partial_train: null
+
+# Traning 
+epochs: 300
+batch_size: 32 
+lr_backbone: 0.0001
+lr_head: 0.001
+weight_decay: 0.01
+lr_decay: 0.99
+
+# Model
+model_version: class
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+num_joints: 17
+hidden_dim: 2048
+dropout_ratio: 0.5
+
+# Data
+dataset: ntu60_hrnet
+data_split: xview
+clip_len: 243
+action_classes: 60
+
+# Augmentation
+random_move: True
+scale_range_train: [1, 3]
+scale_range_test: [2, 2]

configs/action/MB_train_NTU120_oneshot.yaml

@@ -0,0 +1,35 @@
+# General  
+finetune: False
+partial_train: null
+
+# Traning 
+n_views: 2
+temp: 0.1
+
+epochs: 50
+batch_size: 32 
+lr_backbone: 0.0001
+lr_head: 0.001
+weight_decay: 0.01
+lr_decay: 0.99
+
+# Model
+model_version: embed
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+num_joints: 17
+hidden_dim: 2048
+dropout_ratio: 0.1
+
+# Data
+clip_len: 100
+
+# Augmentation
+random_move: True
+scale_range_train: [1, 3]
+scale_range_test: [2, 2]

configs/action/MB_train_NTU60_xsub.yaml

@@ -0,0 +1,35 @@
+# General  
+finetune: False
+partial_train: null
+
+# Traning 
+epochs: 300
+batch_size: 32 
+lr_backbone: 0.0001
+lr_head: 0.0001
+weight_decay: 0.01
+lr_decay: 0.99
+
+# Model
+model_version: class
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+num_joints: 17
+hidden_dim: 2048
+dropout_ratio: 0.5
+
+# Data
+dataset: ntu60_hrnet
+data_split: xsub
+clip_len: 243
+action_classes: 60
+
+# Augmentation
+random_move: True
+scale_range_train: [1, 3]
+scale_range_test: [2, 2]

configs/action/MB_train_NTU60_xview.yaml

@@ -0,0 +1,35 @@
+# General  
+finetune: False
+partial_train: null
+
+# Traning 
+epochs: 300
+batch_size: 32 
+lr_backbone: 0.0001
+lr_head: 0.0001
+weight_decay: 0.01
+lr_decay: 0.99
+
+# Model
+model_version: class
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+num_joints: 17
+hidden_dim: 2048
+dropout_ratio: 0.5
+
+# Data
+dataset: ntu60_hrnet
+data_split: xview
+clip_len: 243
+action_classes: 60
+
+# Augmentation
+random_move: True
+scale_range_train: [1, 3]
+scale_range_test: [2, 2]

configs/mesh/MB_ft_h36m.yaml

@@ -0,0 +1,51 @@
+# General  
+finetune: True
+partial_train: null
+train_pw3d: False
+warmup_h36m: 100
+
+# Traning 
+epochs: 60
+checkpoint_frequency: 20
+batch_size: 128
+batch_size_img: 512
+dropout: 0.1
+dropout_loc: 1
+lr_backbone: 0.00005
+lr_head: 0.0005
+weight_decay: 0.01
+lr_decay: 0.98
+
+# Model
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+hidden_dim: 1024
+
+# Data
+data_root: data/mesh
+dt_file_h36m: mesh_det_h36m.pkl
+clip_len: 16
+data_stride: 8
+sample_stride: 1
+num_joints: 17
+
+# Loss
+lambda_3d: 0.5
+lambda_scale: 0
+lambda_3dv: 10
+lambda_lv: 0
+lambda_lg: 0
+lambda_a: 0
+lambda_av: 0
+lambda_pose: 1000
+lambda_shape: 1
+lambda_norm: 20
+loss_type: 'L1'
+
+# Augmentation
+flip: True

configs/mesh/MB_ft_pw3d.yaml

@@ -0,0 +1,53 @@
+# General  
+finetune: True
+partial_train: null
+train_pw3d: True
+warmup_h36m: 20
+warmup_coco: 100
+
+# Traning 
+epochs: 60
+checkpoint_frequency: 20
+batch_size: 128
+batch_size_img: 512
+dropout: 0.1
+lr_backbone: 0.00005
+lr_head: 0.0005
+weight_decay: 0.01
+lr_decay: 0.98
+
+# Model
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+hidden_dim: 1024
+
+# Data
+data_root: data/mesh
+dt_file_h36m: mesh_det_h36m.pkl
+dt_file_coco: mesh_det_coco.pkl
+dt_file_pw3d: mesh_det_pw3d.pkl
+clip_len: 16
+data_stride: 8
+sample_stride: 1
+num_joints: 17
+
+# Loss
+lambda_3d: 0.5
+lambda_scale: 0
+lambda_3dv: 10
+lambda_lv: 0
+lambda_lg: 0
+lambda_a: 0
+lambda_av: 0
+lambda_pose: 1000
+lambda_shape: 1
+lambda_norm: 20
+loss_type: 'L1'
+
+# Augmentation
+flip: True

configs/mesh/MB_train_h36m.yaml

@@ -0,0 +1,51 @@
+# General  
+finetune: False
+partial_train: null
+train_pw3d: False
+warmup_h36m: 100
+
+# Traning 
+epochs: 100
+checkpoint_frequency: 20
+batch_size: 128
+batch_size_img: 512
+dropout: 0.1
+dropout_loc: 1
+lr_backbone: 0.0001
+lr_head: 0.0001
+weight_decay: 0.01
+lr_decay: 0.98
+
+# Model
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+hidden_dim: 1024
+
+# Data
+data_root: data/mesh
+dt_file_h36m: mesh_det_h36m.pkl
+clip_len: 16
+data_stride: 8
+sample_stride: 1
+num_joints: 17
+
+# Loss
+lambda_3d: 0.5
+lambda_scale: 0
+lambda_3dv: 10
+lambda_lv: 0
+lambda_lg: 0
+lambda_a: 0
+lambda_av: 0
+lambda_pose: 1000
+lambda_shape: 1
+lambda_norm: 20
+loss_type: 'L1'
+
+# Augmentation
+flip: True

configs/mesh/MB_train_pw3d.yaml

@@ -0,0 +1,53 @@
+# General  
+finetune: False
+partial_train: null
+train_pw3d: True
+warmup_h36m: 20
+warmup_coco: 100
+
+# Traning 
+epochs: 60
+checkpoint_frequency: 20
+batch_size: 128
+batch_size_img: 512
+dropout: 0.1
+lr_backbone: 0.0001
+lr_head: 0.0001
+weight_decay: 0.01
+lr_decay: 0.98
+
+# Model
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+hidden_dim: 1024
+
+# Data
+data_root: data/mesh
+dt_file_h36m: mesh_det_h36m.pkl
+dt_file_coco: mesh_det_coco.pkl
+dt_file_pw3d: mesh_det_pw3d.pkl
+clip_len: 16
+data_stride: 8
+sample_stride: 1
+num_joints: 17
+
+# Loss
+lambda_3d: 0.5
+lambda_scale: 0
+lambda_3dv: 10
+lambda_lv: 0
+lambda_lg: 0
+lambda_a: 0
+lambda_av: 0
+lambda_pose: 1000
+lambda_shape: 1
+lambda_norm: 20
+loss_type: 'L1'
+
+# Augmentation
+flip: True

configs/pose3d/MB_ft_h36m.yaml

@@ -0,0 +1,50 @@
+# General  
+train_2d: False
+no_eval: False
+finetune: True
+partial_train: null
+
+# Traning 
+epochs: 60
+checkpoint_frequency: 30
+batch_size: 32 
+dropout: 0.0
+learning_rate: 0.0002
+weight_decay: 0.01
+lr_decay: 0.99
+
+# Model
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+
+# Data
+data_root: data/motion3d/MB3D_f243s81/
+subset_list: [H36M-SH]
+dt_file: h36m_sh_conf_cam_source_final.pkl
+clip_len: 243
+data_stride: 81
+rootrel: True
+sample_stride: 1
+num_joints: 17
+no_conf: False
+gt_2d: False
+
+# Loss
+lambda_3d_velocity: 20.0
+lambda_scale: 0.5
+lambda_lv: 0.0
+lambda_lg: 0.0
+lambda_a: 0.0
+lambda_av: 0.0
+
+# Augmentation
+synthetic: False
+flip: True
+mask_ratio: 0.
+mask_T_ratio: 0.
+noise: False

configs/pose3d/MB_ft_h36m_global.yaml

@@ -0,0 +1,50 @@
+# General  
+train_2d: False
+no_eval: False
+finetune: True
+partial_train: null
+
+# Traning 
+epochs: 60
+checkpoint_frequency: 30
+batch_size: 32 
+dropout: 0.0
+learning_rate: 0.0002
+weight_decay: 0.01
+lr_decay: 0.99
+
+# Model
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+
+# Data
+data_root: data/motion3d/MB3D_f243s81/
+subset_list: [H36M-SH]
+dt_file: h36m_sh_conf_cam_source_final.pkl
+clip_len: 243
+data_stride: 81
+rootrel: False
+sample_stride: 1
+num_joints: 17
+no_conf: False
+gt_2d: False
+
+# Loss
+lambda_3d_velocity: 20.0
+lambda_scale: 0.5
+lambda_lv: 0.0
+lambda_lg: 0.0
+lambda_a: 0.0
+lambda_av: 0.0
+
+# Augmentation
+synthetic: False
+flip: True
+mask_ratio: 0.
+mask_T_ratio: 0.
+noise: False

configs/pose3d/MB_ft_h36m_global_lite.yaml

@@ -0,0 +1,50 @@
+# General  
+train_2d: False
+no_eval: False
+finetune: True
+partial_train: null
+
+# Traning 
+epochs: 60
+checkpoint_frequency: 30
+batch_size: 32 
+dropout: 0.0
+learning_rate: 0.0005
+weight_decay: 0.01
+lr_decay: 0.99
+
+# Model
+maxlen: 243
+dim_feat: 256
+mlp_ratio: 4
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+
+# Data
+data_root: data/motion3d/MB3D_f243s81/
+subset_list: [H36M-SH]
+dt_file: h36m_sh_conf_cam_source_final.pkl
+clip_len: 243
+data_stride: 81
+rootrel: False
+sample_stride: 1
+num_joints: 17
+no_conf: False
+gt_2d: False
+
+# Loss
+lambda_3d_velocity: 20.0
+lambda_scale: 0.5
+lambda_lv: 0.0
+lambda_lg: 0.0
+lambda_a: 0.0
+lambda_av: 0.0
+
+# Augmentation
+synthetic: False
+flip: True
+mask_ratio: 0.
+mask_T_ratio: 0.
+noise: False

configs/pose3d/MB_train_h36m.yaml

@@ -0,0 +1,51 @@
+# General  
+train_2d: False
+no_eval: False
+finetune: False
+partial_train: null
+
+# Traning 
+epochs: 120
+checkpoint_frequency: 30
+batch_size: 32 
+dropout: 0.0
+learning_rate: 0.0002
+weight_decay: 0.01
+lr_decay: 0.99
+
+# Model
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+
+# Data
+data_root: data/motion3d/MB3D_f243s81/
+subset_list: [H36M-SH]
+dt_file: h36m_sh_conf_cam_source_final.pkl
+clip_len: 243
+data_stride: 81
+rootrel: True
+sample_stride: 1
+num_joints: 17
+no_conf: False
+gt_2d: False
+
+# Loss
+lambda_3d_velocity: 20.0
+lambda_scale: 0.5
+lambda_lv: 0.0
+lambda_lg: 0.0
+lambda_a: 0.0
+lambda_av: 0.0
+
+# Augmentation
+synthetic: False
+flip: True
+mask_ratio: 0.
+mask_T_ratio: 0.
+noise: False
+

configs/pretrain/MB_lite.yaml

@@ -0,0 +1,53 @@
+# General  
+train_2d: True
+no_eval: False
+finetune: False
+partial_train: null
+
+# Traning 
+epochs: 90
+checkpoint_frequency: 30
+batch_size: 64 
+dropout: 0.0
+learning_rate: 0.0005
+weight_decay: 0.01
+lr_decay: 0.99
+pretrain_3d_curriculum: 30
+
+# Model
+maxlen: 243
+dim_feat: 256
+mlp_ratio: 4
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+
+# Data
+data_root: data/motion3d/MB3D_f243s81/
+subset_list: [AMASS, H36M-SH]
+dt_file: h36m_sh_conf_cam_source_final.pkl
+clip_len: 243
+data_stride: 81
+rootrel: True
+sample_stride: 1
+num_joints: 17
+no_conf: False
+gt_2d: False
+
+# Loss
+lambda_3d_velocity: 20.0
+lambda_scale: 0.5
+lambda_lv: 0.0
+lambda_lg: 0.0
+lambda_a: 0.0
+lambda_av: 0.0
+
+# Augmentation
+synthetic: True   # synthetic: don't use 2D detection results, fake it (from 3D)
+flip: True
+mask_ratio: 0.05
+mask_T_ratio: 0.1
+noise: True
+noise_path: params/synthetic_noise.pth
+d2c_params_path: params/d2c_params.pkl

configs/pretrain/MB_pretrain.yaml

@@ -0,0 +1,53 @@
+# General  
+train_2d: True
+no_eval: False
+finetune: False
+partial_train: null
+
+# Traning 
+epochs: 90
+checkpoint_frequency: 30
+batch_size: 64 
+dropout: 0.0
+learning_rate: 0.0005
+weight_decay: 0.01
+lr_decay: 0.99
+pretrain_3d_curriculum: 30
+
+# Model
+maxlen: 243
+dim_feat: 512
+mlp_ratio: 2
+depth: 5
+dim_rep: 512
+num_heads: 8
+att_fuse: True
+
+# Data
+data_root: data/motion3d/MB3D_f243s81/
+subset_list: [AMASS, H36M-SH]
+dt_file: h36m_sh_conf_cam_source_final.pkl
+clip_len: 243
+data_stride: 81
+rootrel: True
+sample_stride: 1
+num_joints: 17
+no_conf: False
+gt_2d: False
+
+# Loss
+lambda_3d_velocity: 20.0
+lambda_scale: 0.5
+lambda_lv: 0.0
+lambda_lg: 0.0
+lambda_a: 0.0
+lambda_av: 0.0
+
+# Augmentation
+synthetic: True   # synthetic: don't use 2D detection results, fake it (from 3D)
+flip: True
+mask_ratio: 0.05
+mask_T_ratio: 0.1
+noise: True
+noise_path: params/synthetic_noise.pth
+d2c_params_path: params/d2c_params.pkl

docs/action.md

@@ -0,0 +1,86 @@
+# Skeleton-based Action Recognition
+
+## Data
+
+The NTURGB+D 2D detection results are provided by [pyskl](https://github.com/kennymckormick/pyskl/blob/main/tools/data/README.md) using HRNet.
+
+1. Download [`ntu60_hrnet.pkl`](https://download.openmmlab.com/mmaction/pyskl/data/nturgbd/ntu60_hrnet.pkl) and  [`ntu120_hrnet.pkl`](https://download.openmmlab.com/mmaction/pyskl/data/nturgbd/ntu120_hrnet.pkl) to  `data/action/`. 
+2. Download the 1-shot split [here](https://1drv.ms/f/s!AvAdh0LSjEOlfi-hqlHxdVMZxWM) and put it to  `data/action/`. 
+
+## Running
+
+### NTURGB+D
+
+**Train from scratch:**
+
+```shell
+# Cross-subject
+python train_action.py \
+--config configs/action/MB_train_NTU60_xsub.yaml \
+--checkpoint checkpoint/action/MB_train_NTU60_xsub
+
+# Cross-view
+python train_action.py \
+--config configs/action/MB_train_NTU60_xview.yaml \
+--checkpoint checkpoint/action/MB_train_NTU60_xview
+```
+
+**Finetune from pretrained MotionBERT:**
+
+```shell
+# Cross-subject
+python train_action.py \
+--config configs/action/MB_ft_NTU60_xsub.yaml \
+--pretrained checkpoint/pretrain/MB_release \
+--checkpoint checkpoint/action/FT_MB_release_MB_ft_NTU60_xsub
+
+# Cross-view
+python train_action.py \
+--config configs/action/MB_ft_NTU60_xview.yaml \
+--pretrained checkpoint/pretrain/MB_release \
+--checkpoint checkpoint/action/FT_MB_release_MB_ft_NTU60_xview
+```
+
+**Evaluate:**
+
+```bash
+# Cross-subject
+python train_action.py \
+--config configs/action/MB_train_NTU60_xsub.yaml \
+--evaluate checkpoint/action/MB_train_NTU60_xsub/best_epoch.bin 
+
+# Cross-view
+python train_action.py \
+--config configs/action/MB_train_NTU60_xview.yaml \
+--evaluate checkpoint/action/MB_train_NTU60_xview/best_epoch.bin 
+```
+
+### NTURGB+D-120 (1-shot)
+
+**Train from scratch:**
+
+```bash
+python train_action_1shot.py \
+--config configs/action/MB_train_NTU120_oneshot.yaml \
+--checkpoint checkpoint/action/MB_train_NTU120_oneshot
+```
+
+**Finetune from a pretrained model:**
+
+```bash
+python train_action_1shot.py \
+--config configs/action/MB_ft_NTU120_oneshot.yaml \
+--pretrained checkpoint/pretrain/MB_release \
+--checkpoint checkpoint/action/FT_MB_release_MB_ft_NTU120_oneshot
+```
+
+**Evaluate:**
+
+```bash
+python train_action_1shot.py \
+--config configs/action/MB_train_NTU120_oneshot.yaml \
+--evaluate checkpoint/action/MB_train_NTU120_oneshot/best_epoch.bin 
+```
+
+
+

docs/inference.md

@@ -0,0 +1,48 @@
+# In-the-wild Inference
+
+## 2D Pose
+
+Please use [AlphaPose](https://github.com/MVIG-SJTU/AlphaPose#quick-start) to extract the 2D keypoints for your video first. We use the *Fast Pose* model trained on *Halpe* dataset ([Link](https://github.com/MVIG-SJTU/AlphaPose/blob/master/docs/MODEL_ZOO.md#halpe-dataset-26-keypoints)).
+
+Note: Currently we only support single person. If your video contains multiple person, you may need to use the [Pose Tracking Module for AlphaPose](https://github.com/MVIG-SJTU/AlphaPose/tree/master/trackers) and set `--focus` to specify the target person id.
+
+
+
+## 3D Pose
+
+| ![pose_1](https://github.com/motionbert/motionbert.github.io/blob/main/assets/pose_1.gif?raw=true) | ![pose_2](https://raw.githubusercontent.com/motionbert/motionbert.github.io/main/assets/pose_2.gif) |
+| ------------------------------------------------------------ | ------------------------------------------------------------ |
+
+
+1. Please download the checkpoint [here](https://1drv.ms/f/s!AvAdh0LSjEOlgT67igq_cIoYvO2y?e=bfEc73) and put it to `checkpoint/pose3d/FT_MB_lite_MB_ft_h36m_global_lite/`.
+1. Run the following command to infer from the extracted 2D poses:
+```bash
+python infer_wild.py \
+--vid_path <your_video.mp4> \
+--json_path <alphapose-results.json> \
+--out_path <output_path>
+```
+
+
+
+## Mesh
+
+| ![mesh_1](https://raw.githubusercontent.com/motionbert/motionbert.github.io/main/assets/mesh_1.gif) | ![mesh_2](https://github.com/motionbert/motionbert.github.io/blob/main/assets/mesh_2.gif?raw=true) |
+| ------------------------------------------------------------ | ----------- |
+
+1. Please download the checkpoint [here](https://1drv.ms/f/s!AvAdh0LSjEOlgTmgYNslCDWMNQi9?e=WjcB1F) and put it to `checkpoint/mesh/FT_MB_release_MB_ft_pw3d/`
+2. Run the following command to infer from the extracted 2D poses:
+```bash
+python infer_wild_mesh.py \
+--vid_path <your_video.mp4> \
+--json_path <alphapose-results.json> \
+--out_path <output_path> \
+--ref_3d_motion_path <3d-pose-results.npy> # Optional, use the estimated 3D motion for root trajectory.
+```
+
+
+
+
+
+
+

docs/mesh.md

@@ -0,0 +1,61 @@
+# Human Mesh Recovery
+
+## Data
+
+1. Download the datasets [here](https://1drv.ms/f/s!AvAdh0LSjEOlfy-hqlHxdVMZxWM) and put them to  `data/mesh/`. We use Human3.6M, COCO, and PW3D for training and testing. Descriptions of the joint regressors could be found in [SPIN](https://github.com/nkolot/SPIN/tree/master/data).
+2. Download the SMPL model(`basicModel_neutral_lbs_10_207_0_v1.0.0.pkl`) from [SMPLify](https://smplify.is.tue.mpg.de/), put it  to `data/mesh/`, and rename it as `SMPL_NEUTRAL.pkl`
+
+
+## Running
+
+**Train from scratch:**
+
+```bash
+# with 3DPW
+python train_mesh.py \
+--config configs/mesh/MB_train_pw3d.yaml \
+--checkpoint checkpoint/mesh/MB_train_pw3d
+
+# H36M
+python train_mesh.py \
+--config configs/mesh/MB_train_h36m.yaml \
+--checkpoint checkpoint/mesh/MB_train_h36m
+```
+
+**Finetune from a pretrained model:**
+
+```bash
+# with 3DPW
+python train_mesh.py \
+--config configs/mesh/MB_ft_pw3d.yaml \
+--pretrained checkpoint/pretrain/MB_release \
+--checkpoint checkpoint/mesh/FT_MB_release_MB_ft_pw3d
+
+# H36M
+python train_mesh.py \
+--config configs/mesh/MB_ft_h36m.yaml \
+--pretrained checkpoint/pretrain/MB_release \
+--checkpoint checkpoint/mesh/FT_MB_release_MB_ft_h36m
+
+```
+
+**Evaluate:**
+
+```bash
+# with 3DPW
+python train_mesh.py \
+--config configs/mesh/MB_train_pw3d.yaml \
+--evaluate checkpoint/mesh/MB_train_pw3d/best_epoch.bin 
+
+# H36M
+python train_mesh.py \
+--config configs/mesh/MB_train_h36m.yaml \
+--evaluate checkpoint/mesh/MB_train_h36m/best_epoch.bin 
+```
+
+
+
+
+
+
+

docs/pose3d.md

@@ -0,0 +1,51 @@
+# 3D Human Pose Estimation
+
+## Data
+
+1. Download the finetuned Stacked Hourglass detections and our preprocessed H3.6M data (.pkl) [here](https://1drv.ms/u/s!AvAdh0LSjEOlgSMvoapR8XVTGcVj) and put it to `data/motion3d`.
+
+  > Note that the preprocessed data is only intended for reproducing our results more easily. If you want to use the dataset, please register to the [Human3.6m website](http://vision.imar.ro/human3.6m/) and download the dataset in its original format. Please refer to [LCN](https://github.com/CHUNYUWANG/lcn-pose#data) for how we prepare the H3.6M data.
+
+2. Slice the motion clips (len=243, stride=81)
+
+   ```bash
+   python tools/convert_h36m.py
+   ```
+
+## Running
+
+**Train from scratch:**
+
+```bash
+python train.py \
+--config configs/pose3d/MB_train_h36m.yaml \
+--checkpoint checkpoint/pose3d/MB_train_h36m
+```
+
+**Finetune from pretrained MotionBERT:**
+
+```bash
+python train.py \
+--config configs/pose3d/MB_ft_h36m.yaml \
+--pretrained checkpoint/pretrain/MB_release \
+--checkpoint checkpoint/pose3d/FT_MB_release_MB_ft_h36m
+```
+
+**Evaluate:**
+
+```bash
+python train.py \
+--config configs/pose3d/MB_train_h36m.yaml \
+--evaluate checkpoint/pose3d/MB_train_h36m/best_epoch.bin         
+```
+
+
+
+
+
+
+
+
+
+
+

docs/pretrain.md

@@ -0,0 +1,59 @@
+# Pretrain
+
+## Data
+
+### AMASS
+
+1. Please download data from the [official website](https://amass.is.tue.mpg.de/download.php) (SMPL+H).
+2. We provide the preprocessing scripts as follows. Minor modifications might be necessary.
+   - [tools/compress_amass.py](../tools/compress_amass.py): downsample the frame rate
+   - [tools/preprocess_amass.py](../tools/preprocess_amass.py): render the mocap data and extract the 3D keypoints
+   - [tools/convert_amass.py](../tools/convert_amass.py): slice them to motion clips
+
+
+### Human 3.6M
+
+Please refer to [pose3d.md](pose3d.md#data).
+
+### InstaVariety
+
+1. Please download data from [human_dynamics](https://github.com/akanazawa/human_dynamics/blob/master/doc/insta_variety.md#generating-tfrecords) to `data/motion2d`.
+1. Use [tools/convert_insta.py](../tools/convert_insta.py) to preprocess the 2D keypoints (need to specify `name_action` ).
+
+### PoseTrack
+
+Please download PoseTrack18 from [MMPose](https://mmpose.readthedocs.io/en/latest/tasks/2d_body_keypoint.html#posetrack18) and unzip to `data/motion2d`.
+
+
+
+The processed directory tree should look like this:
+
+```
+.
+└── data/
+    ├── motion3d/
+    │   └── MB3D_f243s81/
+    │       ├── AMASS
+    │       └── H36M-SH
+    ├── motion2d/
+    │   ├── InstaVariety/
+    │   │   ├── motion_all.npy
+    │   │   └── id_all.npy
+    │   └── posetrack18_annotations/
+    │       ├── train
+    │       └── ...
+    └── ...
+```
+
+
+
+## Train
+
+```bash
+python train.py \
+--config configs/pretrain/MB_pretrain.yaml \
+-c checkpoint/pretrain/MB_pretrain
+```
+
+
+

infer_wild.py

@@ -0,0 +1,97 @@
+import os
+import numpy as np
+import argparse
+from tqdm import tqdm
+import imageio
+import torch
+import torch.nn as nn
+from torch.utils.data import DataLoader
+from lib.utils.tools import *
+from lib.utils.learning import *
+from lib.utils.utils_data import flip_data
+from lib.data.dataset_wild import WildDetDataset
+from lib.utils.vismo import render_and_save
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, default="configs/pose3d/MB_ft_h36m_global_lite.yaml", help="Path to the config file.")
+    parser.add_argument('-e', '--evaluate', default='checkpoint/pose3d/FT_MB_lite_MB_ft_h36m_global_lite/best_epoch.bin', type=str, metavar='FILENAME', help='checkpoint to evaluate (file name)')
+    parser.add_argument('-j', '--json_path', type=str, help='alphapose detection result json path')
+    parser.add_argument('-v', '--vid_path', type=str, help='video path')
+    parser.add_argument('-o', '--out_path', type=str, help='output path')
+    parser.add_argument('--pixel', action='store_true', help='align with pixle coordinates')
+    parser.add_argument('--focus', type=int, default=None, help='target person id')
+    parser.add_argument('--clip_len', type=int, default=243, help='clip length for network input')
+    opts = parser.parse_args()
+    return opts
+
+opts = parse_args()
+args = get_config(opts.config)
+
+model_backbone = load_backbone(args)
+if torch.cuda.is_available():
+    model_backbone = nn.DataParallel(model_backbone)
+    model_backbone = model_backbone.cuda()
+
+print('Loading checkpoint', opts.evaluate)
+checkpoint = torch.load(opts.evaluate, map_location=lambda storage, loc: storage)
+model_backbone.load_state_dict(checkpoint['model_pos'], strict=True)
+model_pos = model_backbone
+model_pos.eval()
+testloader_params = {
+          'batch_size': 1,
+          'shuffle': False,
+          'num_workers': 8,
+          'pin_memory': True,
+          'prefetch_factor': 4,
+          'persistent_workers': True,
+          'drop_last': False
+}
+
+vid = imageio.get_reader(opts.vid_path,  'ffmpeg')
+fps_in = vid.get_meta_data()['fps']
+vid_size = vid.get_meta_data()['size']
+os.makedirs(opts.out_path, exist_ok=True)
+
+if opts.pixel:
+    # Keep relative scale with pixel coornidates
+    wild_dataset = WildDetDataset(opts.json_path, clip_len=opts.clip_len, vid_size=vid_size, scale_range=None, focus=opts.focus)
+else:
+    # Scale to [-1,1]
+    wild_dataset = WildDetDataset(opts.json_path, clip_len=opts.clip_len, scale_range=[1,1], focus=opts.focus)
+
+test_loader = DataLoader(wild_dataset, **testloader_params)
+
+results_all = []
+with torch.no_grad():
+    for batch_input in tqdm(test_loader):
+        N, T = batch_input.shape[:2]
+        if torch.cuda.is_available():
+            batch_input = batch_input.cuda()
+        if args.no_conf:
+            batch_input = batch_input[:, :, :, :2]
+        if args.flip:    
+            batch_input_flip = flip_data(batch_input)
+            predicted_3d_pos_1 = model_pos(batch_input)
+            predicted_3d_pos_flip = model_pos(batch_input_flip)
+            predicted_3d_pos_2 = flip_data(predicted_3d_pos_flip) # Flip back
+            predicted_3d_pos = (predicted_3d_pos_1 + predicted_3d_pos_2) / 2.0
+        else:
+            predicted_3d_pos = model_pos(batch_input)
+        if args.rootrel:
+            predicted_3d_pos[:,:,0,:]=0                    # [N,T,17,3]
+        else:
+            predicted_3d_pos[:,0,0,2]=0
+            pass
+        if args.gt_2d:
+            predicted_3d_pos[...,:2] = batch_input[...,:2]
+        results_all.append(predicted_3d_pos.cpu().numpy())
+
+results_all = np.hstack(results_all)
+results_all = np.concatenate(results_all)
+render_and_save(results_all, '%s/X3D.mp4' % (opts.out_path), keep_imgs=False, fps=fps_in)
+if opts.pixel:
+    # Convert to pixel coordinates
+    results_all = results_all * (min(vid_size) / 2.0)
+    results_all[:,:,:2] = results_all[:,:,:2] + np.array(vid_size) / 2.0
+np.save('%s/X3D.npy' % (opts.out_path), results_all)

infer_wild_mesh.py

@@ -0,0 +1,157 @@
+import os
+import os.path as osp
+import numpy as np
+import argparse
+import pickle
+from tqdm import tqdm
+import time
+import random
+import imageio
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torch.utils.data import DataLoader
+
+from lib.utils.tools import *
+from lib.utils.learning import *
+from lib.utils.utils_data import flip_data
+from lib.utils.utils_mesh import flip_thetas_batch
+from lib.data.dataset_wild import WildDetDataset
+# from lib.model.loss import *
+from lib.model.model_mesh import MeshRegressor
+from lib.utils.vismo import render_and_save, motion2video_mesh
+from lib.utils.utils_smpl import *
+from scipy.optimize import least_squares
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--config", type=str, default="configs/mesh/MB_ft_pw3d.yaml", help="Path to the config file.")
+    parser.add_argument('-e', '--evaluate', default='checkpoint/mesh/FT_MB_release_MB_ft_pw3d/best_epoch.bin', type=str, metavar='FILENAME', help='checkpoint to evaluate (file name)')
+    parser.add_argument('-j', '--json_path', type=str, help='alphapose detection result json path')
+    parser.add_argument('-v', '--vid_path', type=str, help='video path')
+    parser.add_argument('-o', '--out_path', type=str, help='output path')
+    parser.add_argument('--ref_3d_motion_path', type=str, default=None, help='3D motion path')
+    parser.add_argument('--pixel', action='store_true', help='align with pixle coordinates')
+    parser.add_argument('--focus', type=int, default=None, help='target person id')
+    parser.add_argument('--clip_len', type=int, default=243, help='clip length for network input')
+    opts = parser.parse_args()
+    return opts
+
+def err(p, x, y):
+    return np.linalg.norm(p[0] * x + np.array([p[1], p[2], p[3]]) - y, axis=-1).mean()
+
+def solve_scale(x, y):
+    print('Estimating camera transformation.')
+    best_res = 100000
+    best_scale = None
+    for init_scale in tqdm(range(0,2000,5)):
+        p0 = [init_scale, 0.0, 0.0, 0.0]
+        est = least_squares(err, p0, args = (x.reshape(-1,3), y.reshape(-1,3)))
+        if est['fun'] < best_res:
+            best_res = est['fun']
+            best_scale = est['x'][0]
+    print('Pose matching error = %.2f mm.' % best_res)
+    return best_scale
+
+opts = parse_args()
+args = get_config(opts.config)
+
+# root_rel
+# args.rootrel = True
+
+smpl = SMPL(args.data_root, batch_size=1).cuda()
+J_regressor = smpl.J_regressor_h36m
+
+end = time.time()
+model_backbone = load_backbone(args)
+print(f'init backbone time: {(time.time()-end):02f}s')
+end = time.time()
+model = MeshRegressor(args, backbone=model_backbone, dim_rep=args.dim_rep, hidden_dim=args.hidden_dim, dropout_ratio=args.dropout)
+print(f'init whole model time: {(time.time()-end):02f}s')
+
+if torch.cuda.is_available():
+    model = nn.DataParallel(model)
+    model = model.cuda()
+
+chk_filename = opts.evaluate if opts.evaluate else opts.resume
+print('Loading checkpoint', chk_filename)
+checkpoint = torch.load(chk_filename, map_location=lambda storage, loc: storage)
+model.load_state_dict(checkpoint['model'], strict=True)
+model.eval()
+
+testloader_params = {
+        'batch_size': 1,
+        'shuffle': False,
+        'num_workers': 8,
+        'pin_memory': True,
+        'prefetch_factor': 4,
+        'persistent_workers': True,
+        'drop_last': False
+}
+
+vid = imageio.get_reader(opts.vid_path,  'ffmpeg')
+fps_in = vid.get_meta_data()['fps']
+vid_size = vid.get_meta_data()['size']
+os.makedirs(opts.out_path, exist_ok=True)
+
+if opts.pixel:
+    # Keep relative scale with pixel coornidates
+    wild_dataset = WildDetDataset(opts.json_path, clip_len=opts.clip_len, vid_size=vid_size, scale_range=None, focus=opts.focus)
+else:
+    # Scale to [-1,1]
+    wild_dataset = WildDetDataset(opts.json_path, clip_len=opts.clip_len, scale_range=[1,1], focus=opts.focus)
+
+test_loader = DataLoader(wild_dataset, **testloader_params)
+
+verts_all = []
+reg3d_all = []
+with torch.no_grad():
+    for batch_input in tqdm(test_loader):
+        batch_size, clip_frames = batch_input.shape[:2]
+        if torch.cuda.is_available():
+            batch_input = batch_input.cuda().float()
+        output = model(batch_input)   
+        batch_input_flip = flip_data(batch_input)
+        output_flip = model(batch_input_flip)
+        output_flip_pose = output_flip[0]['theta'][:, :, :72]
+        output_flip_shape = output_flip[0]['theta'][:, :, 72:]
+        output_flip_pose = flip_thetas_batch(output_flip_pose)
+        output_flip_pose = output_flip_pose.reshape(-1, 72)
+        output_flip_shape = output_flip_shape.reshape(-1, 10)
+        output_flip_smpl = smpl(
+            betas=output_flip_shape,
+            body_pose=output_flip_pose[:, 3:],
+            global_orient=output_flip_pose[:, :3],
+            pose2rot=True
+        )
+        output_flip_verts = output_flip_smpl.vertices.detach()
+        J_regressor_batch = J_regressor[None, :].expand(output_flip_verts.shape[0], -1, -1).to(output_flip_verts.device)
+        output_flip_kp3d = torch.matmul(J_regressor_batch, output_flip_verts)  # (NT,17,3) 
+        output_flip_back = [{
+            'verts': output_flip_verts.reshape(batch_size, clip_frames, -1, 3) * 1000.0,
+            'kp_3d': output_flip_kp3d.reshape(batch_size, clip_frames, -1, 3),
+        }]
+        output_final = [{}]
+        for k, v in output_flip_back[0].items():
+            output_final[0][k] = (output[0][k] + output_flip_back[0][k]) / 2.0
+        output = output_final
+        verts_all.append(output[0]['verts'].cpu().numpy())
+        reg3d_all.append(output[0]['kp_3d'].cpu().numpy())
+
+verts_all = np.hstack(verts_all)
+verts_all = np.concatenate(verts_all)
+reg3d_all = np.hstack(reg3d_all)
+reg3d_all = np.concatenate(reg3d_all)
+
+if opts.ref_3d_motion_path:
+    ref_pose = np.load(opts.ref_3d_motion_path)
+    x = ref_pose - ref_pose[:, :1]
+    y = reg3d_all - reg3d_all[:, :1]
+    scale = solve_scale(x, y)
+    root_cam = ref_pose[:, :1] * scale
+    verts_all = verts_all - reg3d_all[:,:1] + root_cam
+
+render_and_save(verts_all, osp.join(opts.out_path, 'mesh.mp4'), keep_imgs=False, fps=fps_in, draw_face=True)
+

lib/data/augmentation.py

@@ -0,0 +1,99 @@
+import numpy as np
+import os
+import random
+import torch
+import copy
+import torch.nn as nn
+from lib.utils.tools import read_pkl
+from lib.utils.utils_data import flip_data, crop_scale_3d
+    
+class Augmenter2D(object):
+    """
+        Make 2D augmentations on the fly. PyTorch batch-processing GPU version.
+    """
+    def __init__(self, args):
+        self.d2c_params = read_pkl(args.d2c_params_path)
+        self.noise = torch.load(args.noise_path)
+        self.mask_ratio = args.mask_ratio
+        self.mask_T_ratio = args.mask_T_ratio
+        self.num_Kframes = 27
+        self.noise_std = 0.002
+
+    def dis2conf(self, dis, a, b, m, s):
+        f = a/(dis+a)+b*dis
+        shift = torch.randn(*dis.shape)*s + m
+        # if torch.cuda.is_available():
+        shift = shift.to(dis.device)
+        return f + shift
+    
+    def add_noise(self, motion_2d):
+        a, b, m, s = self.d2c_params["a"], self.d2c_params["b"], self.d2c_params["m"], self.d2c_params["s"]
+        if "uniform_range" in self.noise.keys():
+            uniform_range = self.noise["uniform_range"]
+        else:
+            uniform_range = 0.06
+        motion_2d = motion_2d[:,:,:,:2]
+        batch_size = motion_2d.shape[0]
+        num_frames = motion_2d.shape[1]
+        num_joints = motion_2d.shape[2]
+        mean = self.noise['mean'].float()
+        std = self.noise['std'].float()
+        weight = self.noise['weight'][:,None].float()
+        sel = torch.rand((batch_size, self.num_Kframes, num_joints, 1))
+        gaussian_sample = (torch.randn(batch_size, self.num_Kframes, num_joints, 2) * std + mean) 
+        uniform_sample = (torch.rand((batch_size, self.num_Kframes, num_joints, 2))-0.5) * uniform_range
+        noise_mean = 0
+        delta_noise = torch.randn(num_frames, num_joints, 2) * self.noise_std + noise_mean
+        # if torch.cuda.is_available():
+        mean = mean.to(motion_2d.device)
+        std = std.to(motion_2d.device)
+        weight = weight.to(motion_2d.device)
+        gaussian_sample = gaussian_sample.to(motion_2d.device)
+        uniform_sample = uniform_sample.to(motion_2d.device)
+        sel = sel.to(motion_2d.device)
+        delta_noise = delta_noise.to(motion_2d.device)
+            
+        delta = gaussian_sample*(sel<weight) + uniform_sample*(sel>=weight)
+        delta_expand = torch.nn.functional.interpolate(delta.unsqueeze(1), [num_frames, num_joints, 2], mode='trilinear', align_corners=True)[:,0]
+        delta_final = delta_expand + delta_noise      
+        motion_2d = motion_2d + delta_final 
+        dx = delta_final[:,:,:,0]
+        dy = delta_final[:,:,:,1]
+        dis2 = dx*dx+dy*dy
+        dis = torch.sqrt(dis2)
+        conf = self.dis2conf(dis, a, b, m, s).clip(0,1).reshape([batch_size, num_frames, num_joints, -1])
+        return torch.cat((motion_2d, conf), dim=3)
+        
+    def add_mask(self, x):
+        ''' motion_2d: (N,T,17,3)
+        '''
+        N,T,J,C = x.shape
+        mask = torch.rand(N,T,J,1, dtype=x.dtype, device=x.device) > self.mask_ratio
+        mask_T = torch.rand(1,T,1,1, dtype=x.dtype, device=x.device) > self.mask_T_ratio
+        x = x * mask * mask_T
+        return x
+    
+    def augment2D(self, motion_2d, mask=False, noise=False):     
+        if noise:
+            motion_2d = self.add_noise(motion_2d)
+        if mask:
+            motion_2d = self.add_mask(motion_2d)
+        return motion_2d
+    
+class Augmenter3D(object):
+    """
+        Make 3D augmentations when dataloaders get items. NumPy single motion version.
+    """
+    def __init__(self, args):
+        self.flip = args.flip
+        if hasattr(args, "scale_range_pretrain"):
+            self.scale_range_pretrain = args.scale_range_pretrain
+        else:
+            self.scale_range_pretrain = None
+    
+    def augment3D(self, motion_3d):
+        if self.scale_range_pretrain:
+            motion_3d = crop_scale_3d(motion_3d, self.scale_range_pretrain)
+        if self.flip and random.random()>0.5:                       
+            motion_3d = flip_data(motion_3d)
+        return motion_3d

lib/data/datareader_h36m.py

@@ -0,0 +1,136 @@
+# Adapted from Optimizing Network Structure for 3D Human Pose Estimation (ICCV 2019) (https://github.com/CHUNYUWANG/lcn-pose/blob/master/tools/data.py)
+
+import numpy as np
+import os, sys
+import random
+import copy
+from lib.utils.tools import read_pkl
+from lib.utils.utils_data import split_clips
+random.seed(0)
+    
+class DataReaderH36M(object):
+    def __init__(self, n_frames, sample_stride, data_stride_train, data_stride_test, read_confidence=True, dt_root = 'data/motion3d', dt_file = 'h36m_cpn_cam_source.pkl'):
+        self.gt_trainset = None
+        self.gt_testset = None
+        self.split_id_train = None
+        self.split_id_test = None
+        self.test_hw = None
+        self.dt_dataset = read_pkl('%s/%s' % (dt_root, dt_file))
+        self.n_frames = n_frames
+        self.sample_stride = sample_stride
+        self.data_stride_train = data_stride_train
+        self.data_stride_test = data_stride_test
+        self.read_confidence = read_confidence
+        
+    def read_2d(self):
+        trainset = self.dt_dataset['train']['joint_2d'][::self.sample_stride, :, :2].astype(np.float32)  # [N, 17, 2]
+        testset = self.dt_dataset['test']['joint_2d'][::self.sample_stride, :, :2].astype(np.float32)  # [N, 17, 2]
+        # map to [-1, 1]
+        for idx, camera_name in enumerate(self.dt_dataset['train']['camera_name']):
+            if camera_name == '54138969' or camera_name == '60457274':
+                res_w, res_h = 1000, 1002
+            elif camera_name == '55011271' or camera_name == '58860488':
+                res_w, res_h = 1000, 1000
+            else:
+                assert 0, '%d data item has an invalid camera name' % idx
+            trainset[idx, :, :] = trainset[idx, :, :] / res_w * 2 - [1, res_h / res_w]
+        for idx, camera_name in enumerate(self.dt_dataset['test']['camera_name']):
+            if camera_name == '54138969' or camera_name == '60457274':
+                res_w, res_h = 1000, 1002
+            elif camera_name == '55011271' or camera_name == '58860488':
+                res_w, res_h = 1000, 1000
+            else:
+                assert 0, '%d data item has an invalid camera name' % idx
+            testset[idx, :, :] = testset[idx, :, :] / res_w * 2 - [1, res_h / res_w]
+        if self.read_confidence:
+            if 'confidence' in self.dt_dataset['train'].keys():
+                train_confidence = self.dt_dataset['train']['confidence'][::self.sample_stride].astype(np.float32)  
+                test_confidence = self.dt_dataset['test']['confidence'][::self.sample_stride].astype(np.float32)  
+                if len(train_confidence.shape)==2: # (1559752, 17)
+                    train_confidence = train_confidence[:,:,None]
+                    test_confidence = test_confidence[:,:,None]
+            else:
+                # No conf provided, fill with 1.
+                train_confidence = np.ones(trainset.shape)[:,:,0:1]
+                test_confidence = np.ones(testset.shape)[:,:,0:1]
+            trainset = np.concatenate((trainset, train_confidence), axis=2)  # [N, 17, 3]
+            testset = np.concatenate((testset, test_confidence), axis=2)  # [N, 17, 3]
+        return trainset, testset
+
+    def read_3d(self):
+        train_labels = self.dt_dataset['train']['joint3d_image'][::self.sample_stride, :, :3].astype(np.float32)  # [N, 17, 3]
+        test_labels = self.dt_dataset['test']['joint3d_image'][::self.sample_stride, :, :3].astype(np.float32)    # [N, 17, 3]
+        # map to [-1, 1]
+        for idx, camera_name in enumerate(self.dt_dataset['train']['camera_name']):
+            if camera_name == '54138969' or camera_name == '60457274':
+                res_w, res_h = 1000, 1002
+            elif camera_name == '55011271' or camera_name == '58860488':
+                res_w, res_h = 1000, 1000
+            else:
+                assert 0, '%d data item has an invalid camera name' % idx
+            train_labels[idx, :, :2] = train_labels[idx, :, :2] / res_w * 2 - [1, res_h / res_w]
+            train_labels[idx, :, 2:] = train_labels[idx, :, 2:] / res_w * 2
+            
+        for idx, camera_name in enumerate(self.dt_dataset['test']['camera_name']):
+            if camera_name == '54138969' or camera_name == '60457274':
+                res_w, res_h = 1000, 1002
+            elif camera_name == '55011271' or camera_name == '58860488':
+                res_w, res_h = 1000, 1000
+            else:
+                assert 0, '%d data item has an invalid camera name' % idx
+            test_labels[idx, :, :2] = test_labels[idx, :, :2] / res_w * 2 - [1, res_h / res_w]
+            test_labels[idx, :, 2:] = test_labels[idx, :, 2:] / res_w * 2
+            
+        return train_labels, test_labels
+    def read_hw(self):
+        if self.test_hw is not None:
+            return self.test_hw
+        test_hw = np.zeros((len(self.dt_dataset['test']['camera_name']), 2))
+        for idx, camera_name in enumerate(self.dt_dataset['test']['camera_name']):
+            if camera_name == '54138969' or camera_name == '60457274':
+                res_w, res_h = 1000, 1002
+            elif camera_name == '55011271' or camera_name == '58860488':
+                res_w, res_h = 1000, 1000
+            else:
+                assert 0, '%d data item has an invalid camera name' % idx
+            test_hw[idx] = res_w, res_h
+        self.test_hw = test_hw
+        return test_hw
+    
+    def get_split_id(self):
+        if self.split_id_train is not None and self.split_id_test is not None:
+            return self.split_id_train, self.split_id_test
+        vid_list_train = self.dt_dataset['train']['source'][::self.sample_stride]                          # (1559752,)
+        vid_list_test = self.dt_dataset['test']['source'][::self.sample_stride]                           # (566920,)
+        self.split_id_train = split_clips(vid_list_train, self.n_frames, data_stride=self.data_stride_train) 
+        self.split_id_test = split_clips(vid_list_test, self.n_frames, data_stride=self.data_stride_test)
+        return self.split_id_train, self.split_id_test
+    
+    def get_hw(self):
+#       Only Testset HW is needed for denormalization
+        test_hw = self.read_hw()                                     # train_data (1559752, 2) test_data (566920, 2)
+        split_id_train, split_id_test = self.get_split_id()
+        test_hw = test_hw[split_id_test][:,0,:]                      # (N, 2)
+        return test_hw
+    
+    def get_sliced_data(self):
+        train_data, test_data = self.read_2d()     # train_data (1559752, 17, 3) test_data (566920, 17, 3)
+        train_labels, test_labels = self.read_3d() # train_labels (1559752, 17, 3) test_labels (566920, 17, 3)
+        split_id_train, split_id_test = self.get_split_id()
+        train_data, test_data = train_data[split_id_train], test_data[split_id_test]                # (N, 27, 17, 3)
+        train_labels, test_labels = train_labels[split_id_train], test_labels[split_id_test]        # (N, 27, 17, 3)
+        # ipdb.set_trace()
+        return train_data, test_data, train_labels, test_labels
+    
+    def denormalize(self, test_data):
+#       data: (N, n_frames, 51) or data: (N, n_frames, 17, 3)        
+        n_clips = test_data.shape[0]
+        test_hw = self.get_hw()
+        data = test_data.reshape([n_clips, -1, 17, 3])
+        assert len(data) == len(test_hw)
+        # denormalize (x,y,z) coordiantes for results
+        for idx, item in enumerate(data):
+            res_w, res_h = test_hw[idx]
+            data[idx, :, :, :2] = (data[idx, :, :, :2] + np.array([1, res_h / res_w])) * res_w / 2
+            data[idx, :, :, 2:] = data[idx, :, :, 2:] * res_w / 2
+        return data # [n_clips, -1, 17, 3]

lib/data/datareader_mesh.py

@@ -0,0 +1,59 @@
+import numpy as np
+import os, sys
+import copy
+from lib.utils.tools import read_pkl
+from lib.utils.utils_data import split_clips
+
+class DataReaderMesh(object):
+    def __init__(self, n_frames, sample_stride, data_stride_train, data_stride_test, read_confidence=True, dt_root = 'data/mesh', dt_file = 'pw3d_det.pkl', res=[1920, 1920]):
+        self.split_id_train = None
+        self.split_id_test = None
+        self.dt_dataset = read_pkl('%s/%s' % (dt_root, dt_file))
+        self.n_frames = n_frames
+        self.sample_stride = sample_stride
+        self.data_stride_train = data_stride_train
+        self.data_stride_test = data_stride_test
+        self.read_confidence = read_confidence
+        self.res = res
+        
+    def read_2d(self):
+        if self.res is not None:
+            res_w, res_h = self.res
+            offset = [1, res_h / res_w]
+        else:
+            res = np.array(self.dt_dataset['train']['img_hw'])[::self.sample_stride].astype(np.float32)
+            res_w, res_h = res.max(1)[:, None, None], res.max(1)[:, None, None]
+            offset = 1
+        trainset = self.dt_dataset['train']['joint_2d'][::self.sample_stride, :, :2].astype(np.float32)  # [N, 17, 2]
+        testset = self.dt_dataset['test']['joint_2d'][::self.sample_stride, :, :2].astype(np.float32)    # [N, 17, 2] 
+        # res_w, res_h = self.res
+        trainset = trainset / res_w * 2 - offset
+        testset = testset / res_w * 2 - offset
+        if self.read_confidence:
+            train_confidence = self.dt_dataset['train']['confidence'][::self.sample_stride].astype(np.float32)  
+            test_confidence = self.dt_dataset['test']['confidence'][::self.sample_stride].astype(np.float32)  
+            if len(train_confidence.shape)==2: 
+                train_confidence = train_confidence[:,:,None]
+                test_confidence = test_confidence[:,:,None]
+            trainset = np.concatenate((trainset, train_confidence), axis=2)  # [N, 17, 3]
+            testset = np.concatenate((testset, test_confidence), axis=2)  # [N, 17, 3]
+        return trainset, testset
+    
+    def get_split_id(self):
+        if self.split_id_train is not None and self.split_id_test is not None:
+            return self.split_id_train, self.split_id_test
+        vid_list_train = self.dt_dataset['train']['source'][::self.sample_stride]                          
+        vid_list_test = self.dt_dataset['test']['source'][::self.sample_stride]                          
+        self.split_id_train = split_clips(vid_list_train, self.n_frames, self.data_stride_train)  
+        self.split_id_test = split_clips(vid_list_test, self.n_frames, self.data_stride_test)  
+        return self.split_id_train, self.split_id_test
+    
+    def get_sliced_data(self):
+        train_data, test_data = self.read_2d()     
+        train_labels, test_labels = self.read_3d() 
+        split_id_train, split_id_test = self.get_split_id()
+        train_data, test_data = train_data[split_id_train], test_data[split_id_test]                     # (N, 27, 17, 3)
+        train_labels, test_labels = train_labels[split_id_train], test_labels[split_id_test]             # (N, 27, 17, 3)
+        return train_data, test_data, train_labels, test_labels
+
+    

lib/data/dataset_action.py

@@ -0,0 +1,206 @@
+import torch
+import numpy as np
+import os
+import random
+import copy
+from torch.utils.data import Dataset, DataLoader
+from lib.utils.utils_data import crop_scale, resample
+from lib.utils.tools import read_pkl
+    
+def get_action_names(file_path = "data/action/ntu_actions.txt"):
+    f = open(file_path, "r")
+    s = f.read()
+    actions = s.split('\n')
+    action_names = []
+    for a in actions:
+        action_names.append(a.split('.')[1][1:])
+    return action_names
+
+def make_cam(x, img_shape):
+    '''
+        Input: x (M x T x V x C)
+               img_shape (height, width)
+    '''
+    h, w = img_shape
+    if w >= h:
+        x_cam = x / w * 2 - 1
+    else:
+        x_cam = x / h * 2 - 1
+    return x_cam
+
+def coco2h36m(x):
+    '''
+        Input: x (M x T x V x C)
+        
+        COCO: {0-nose 1-Leye 2-Reye 3-Lear 4Rear 5-Lsho 6-Rsho 7-Lelb 8-Relb 9-Lwri 10-Rwri 11-Lhip 12-Rhip 13-Lkne 14-Rkne 15-Lank 16-Rank}
+        
+        H36M:
+        0: 'root',
+        1: 'rhip',
+        2: 'rkne',
+        3: 'rank',
+        4: 'lhip',
+        5: 'lkne',
+        6: 'lank',
+        7: 'belly',
+        8: 'neck',
+        9: 'nose',
+        10: 'head',
+        11: 'lsho',
+        12: 'lelb',
+        13: 'lwri',
+        14: 'rsho',
+        15: 'relb',
+        16: 'rwri'
+    '''
+    y = np.zeros(x.shape)
+    y[:,:,0,:] = (x[:,:,11,:] + x[:,:,12,:]) * 0.5
+    y[:,:,1,:] = x[:,:,12,:]
+    y[:,:,2,:] = x[:,:,14,:]
+    y[:,:,3,:] = x[:,:,16,:]
+    y[:,:,4,:] = x[:,:,11,:]
+    y[:,:,5,:] = x[:,:,13,:]
+    y[:,:,6,:] = x[:,:,15,:]
+    y[:,:,8,:] = (x[:,:,5,:] + x[:,:,6,:]) * 0.5
+    y[:,:,7,:] = (y[:,:,0,:] + y[:,:,8,:]) * 0.5
+    y[:,:,9,:] = x[:,:,0,:]
+    y[:,:,10,:] = (x[:,:,1,:] + x[:,:,2,:]) * 0.5
+    y[:,:,11,:] = x[:,:,5,:]
+    y[:,:,12,:] = x[:,:,7,:]
+    y[:,:,13,:] = x[:,:,9,:]
+    y[:,:,14,:] = x[:,:,6,:]
+    y[:,:,15,:] = x[:,:,8,:]
+    y[:,:,16,:] = x[:,:,10,:]
+    return y
+    
+def random_move(data_numpy,
+                angle_range=[-10., 10.],
+                scale_range=[0.9, 1.1],
+                transform_range=[-0.1, 0.1],
+                move_time_candidate=[1]):
+    data_numpy = np.transpose(data_numpy, (3,1,2,0)) # M,T,V,C-> C,T,V,M
+    C, T, V, M = data_numpy.shape
+    move_time = random.choice(move_time_candidate)
+    node = np.arange(0, T, T * 1.0 / move_time).round().astype(int)
+    node = np.append(node, T)
+    num_node = len(node)
+    A = np.random.uniform(angle_range[0], angle_range[1], num_node)
+    S = np.random.uniform(scale_range[0], scale_range[1], num_node)
+    T_x = np.random.uniform(transform_range[0], transform_range[1], num_node)
+    T_y = np.random.uniform(transform_range[0], transform_range[1], num_node)
+    a = np.zeros(T)
+    s = np.zeros(T)
+    t_x = np.zeros(T)
+    t_y = np.zeros(T)
+    # linspace
+    for i in range(num_node - 1):
+        a[node[i]:node[i + 1]] = np.linspace(
+            A[i], A[i + 1], node[i + 1] - node[i]) * np.pi / 180
+        s[node[i]:node[i + 1]] = np.linspace(S[i], S[i + 1], node[i + 1] - node[i])
+        t_x[node[i]:node[i + 1]] = np.linspace(T_x[i], T_x[i + 1], node[i + 1] - node[i])
+        t_y[node[i]:node[i + 1]] = np.linspace(T_y[i], T_y[i + 1], node[i + 1] - node[i])
+    theta = np.array([[np.cos(a) * s, -np.sin(a) * s],
+                      [np.sin(a) * s, np.cos(a) * s]])
+    # perform transformation
+    for i_frame in range(T):
+        xy = data_numpy[0:2, i_frame, :, :]
+        new_xy = np.dot(theta[:, :, i_frame], xy.reshape(2, -1))
+        new_xy[0] += t_x[i_frame]
+        new_xy[1] += t_y[i_frame]
+        data_numpy[0:2, i_frame, :, :] = new_xy.reshape(2, V, M)
+    data_numpy = np.transpose(data_numpy, (3,1,2,0)) # C,T,V,M -> M,T,V,C
+    return data_numpy    
+
+def human_tracking(x):
+    M, T = x.shape[:2]
+    if M==1:
+        return x
+    else:
+        diff0 = np.sum(np.linalg.norm(x[0,1:] - x[0,:-1], axis=-1), axis=-1)        # (T-1, V, C) -> (T-1)
+        diff1 = np.sum(np.linalg.norm(x[0,1:] - x[1,:-1], axis=-1), axis=-1)
+        x_new = np.zeros(x.shape)
+        sel = np.cumsum(diff0 > diff1) % 2
+        sel = sel[:,None,None]
+        x_new[0][0] = x[0][0]
+        x_new[1][0] = x[1][0]
+        x_new[0,1:] = x[1,1:] * sel + x[0,1:] * (1-sel)
+        x_new[1,1:] = x[0,1:] * sel + x[1,1:] * (1-sel)
+        return x_new
+
+class ActionDataset(Dataset):
+    def __init__(self, data_path, data_split, n_frames=243, random_move=True, scale_range=[1,1], check_split=True):   # data_split: train/test etc.
+        np.random.seed(0)
+        dataset = read_pkl(data_path)
+        if check_split:
+            assert data_split in dataset['split'].keys()
+            self.split = dataset['split'][data_split]
+        annotations = dataset['annotations']
+        self.random_move = random_move
+        self.is_train = "train" in data_split or (check_split==False)
+        if "oneshot" in data_split:
+            self.is_train = False
+        self.scale_range = scale_range
+        motions = []
+        labels = []
+        for sample in annotations:
+            if check_split and (not sample['frame_dir'] in self.split):
+                continue
+            resample_id = resample(ori_len=sample['total_frames'], target_len=n_frames, randomness=self.is_train)
+            motion_cam = make_cam(x=sample['keypoint'], img_shape=sample['img_shape'])
+            motion_cam = human_tracking(motion_cam)
+            motion_cam = coco2h36m(motion_cam)
+            motion_conf = sample['keypoint_score'][..., None]
+            motion = np.concatenate((motion_cam[:,resample_id], motion_conf[:,resample_id]), axis=-1)
+            if motion.shape[0]==1:                                  # Single person, make a fake zero person
+                fake = np.zeros(motion.shape)
+                motion = np.concatenate((motion, fake), axis=0)
+            motions.append(motion.astype(np.float32)) 
+            labels.append(sample['label'])
+        self.motions = np.array(motions)
+        self.labels = np.array(labels)
+        
+    def __len__(self):
+        'Denotes the total number of samples'
+        return len(self.motions)
+
+    def __getitem__(self, index):
+        raise NotImplementedError 
+
+class NTURGBD(ActionDataset):
+    def __init__(self, data_path, data_split, n_frames=243, random_move=True, scale_range=[1,1]):
+        super(NTURGBD, self).__init__(data_path, data_split, n_frames, random_move, scale_range)
+        
+    def __getitem__(self, idx):
+        'Generates one sample of data'
+        motion, label = self.motions[idx], self.labels[idx] # (M,T,J,C)
+        if self.random_move:
+            motion = random_move(motion)
+        if self.scale_range:
+            result = crop_scale(motion, scale_range=self.scale_range)
+        else:
+            result = motion
+        return result.astype(np.float32), label
+    
+class NTURGBD1Shot(ActionDataset):
+    def __init__(self, data_path, data_split, n_frames=243, random_move=True, scale_range=[1,1], check_split=False):
+        super(NTURGBD1Shot, self).__init__(data_path, data_split, n_frames, random_move, scale_range, check_split)
+        oneshot_classes = [0, 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, 72, 78, 84, 90, 96, 102, 108, 114]
+        new_classes = set(range(120)) - set(oneshot_classes)
+        old2new = {}
+        for i, cid in enumerate(new_classes):
+            old2new[cid] = i
+        filtered = [not (x in oneshot_classes) for x in self.labels]
+        self.motions = self.motions[filtered]
+        filtered_labels = self.labels[filtered]
+        self.labels = [old2new[x] for x in filtered_labels]
+        
+    def __getitem__(self, idx):
+        'Generates one sample of data'
+        motion, label = self.motions[idx], self.labels[idx] # (M,T,J,C)
+        if self.random_move:
+            motion = random_move(motion)
+        if self.scale_range:
+            result = crop_scale(motion, scale_range=self.scale_range)
+        else:
+            result = motion
+        return result.astype(np.float32), label

lib/data/dataset_mesh.py

@@ -0,0 +1,97 @@
+import torch
+import numpy as np
+import glob
+import os
+import io
+import random
+import pickle
+from torch.utils.data import Dataset, DataLoader
+from lib.data.augmentation import Augmenter3D
+from lib.utils.tools import read_pkl
+from lib.utils.utils_data import flip_data, crop_scale
+from lib.utils.utils_mesh import flip_thetas
+from lib.utils.utils_smpl import SMPL
+from torch.utils.data import Dataset, DataLoader
+from lib.data.datareader_h36m import DataReaderH36M  
+from lib.data.datareader_mesh import DataReaderMesh  
+from lib.data.dataset_action import random_move  
+
+class SMPLDataset(Dataset):
+    def __init__(self, args, data_split, dataset): # data_split: train/test; dataset: h36m, coco, pw3d
+        random.seed(0)
+        np.random.seed(0)
+        self.clip_len = args.clip_len
+        self.data_split = data_split
+        if dataset=="h36m":
+            datareader = DataReaderH36M(n_frames=self.clip_len, sample_stride=args.sample_stride, data_stride_train=args.data_stride, data_stride_test=self.clip_len, dt_root=args.data_root, dt_file=args.dt_file_h36m)
+        elif dataset=="coco":
+            datareader = DataReaderMesh(n_frames=1, sample_stride=args.sample_stride, data_stride_train=1, data_stride_test=1, dt_root=args.data_root, dt_file=args.dt_file_coco, res=[640, 640])
+        elif dataset=="pw3d":
+            datareader = DataReaderMesh(n_frames=self.clip_len, sample_stride=args.sample_stride, data_stride_train=args.data_stride, data_stride_test=self.clip_len, dt_root=args.data_root, dt_file=args.dt_file_pw3d, res=[1920, 1920])
+        else:
+            raise Exception("Mesh dataset undefined.")
+
+        split_id_train, split_id_test = datareader.get_split_id()                        # Index of clips
+        train_data, test_data = datareader.read_2d()
+        train_data, test_data = train_data[split_id_train], test_data[split_id_test]     # Input: (N, T, 17, 3)
+        self.motion_2d = {'train': train_data, 'test': test_data}[data_split]
+
+        dt = datareader.dt_dataset
+        smpl_pose_train = dt['train']['smpl_pose'][split_id_train]                       # (N, T, 72)
+        smpl_shape_train = dt['train']['smpl_shape'][split_id_train]                     # (N, T, 10)
+        smpl_pose_test = dt['test']['smpl_pose'][split_id_test]                          # (N, T, 72)
+        smpl_shape_test = dt['test']['smpl_shape'][split_id_test]                        # (N, T, 10)
+        
+        self.motion_smpl_3d = {'train': {'pose': smpl_pose_train, 'shape': smpl_shape_train}, 'test': {'pose': smpl_pose_test, 'shape': smpl_shape_test}}[data_split]
+        self.smpl = SMPL(
+            args.data_root,
+            batch_size=1,
+        )
+
+    def __len__(self):
+        'Denotes the total number of samples'
+        return len(self.motion_2d)
+
+    def __getitem__(self, index):
+        raise NotImplementedError 
+
+class MotionSMPL(SMPLDataset):
+    def __init__(self, args, data_split, dataset):
+        super(MotionSMPL, self).__init__(args, data_split, dataset)
+        self.flip = args.flip
+        
+    def __getitem__(self, index):
+        'Generates one sample of data'
+        # Select sample
+        motion_2d = self.motion_2d[index]                                            # motion_2d: (T,17,3)     
+        motion_2d[:,:,2] = np.clip(motion_2d[:,:,2], 0, 1)
+        motion_smpl_pose = self.motion_smpl_3d['pose'][index].reshape(-1, 24, 3)     # motion_smpl_3d: (T, 24, 3)    
+        motion_smpl_shape = self.motion_smpl_3d['shape'][index]                      # motion_smpl_3d: (T,10)    
+        
+        if self.data_split=="train":
+            if self.flip and random.random() > 0.5:                                  # Training augmentation - random flipping
+                motion_2d = flip_data(motion_2d)
+                motion_smpl_pose = flip_thetas(motion_smpl_pose)                
+
+            
+        motion_smpl_pose = torch.from_numpy(motion_smpl_pose).reshape(-1, 72).float()
+        motion_smpl_shape = torch.from_numpy(motion_smpl_shape).reshape(-1, 10).float()
+        motion_smpl = self.smpl(
+            betas=motion_smpl_shape,
+            body_pose=motion_smpl_pose[:, 3:],
+            global_orient=motion_smpl_pose[:, :3],
+            pose2rot=True
+        )
+        motion_verts = motion_smpl.vertices.detach()*1000.0
+        J_regressor = self.smpl.J_regressor_h36m
+        J_regressor_batch = J_regressor[None, :].expand(motion_verts.shape[0], -1, -1).to(motion_verts.device)
+        motion_3d_reg = torch.matmul(J_regressor_batch, motion_verts)                 # motion_3d: (T,17,3)  
+        motion_verts = motion_verts - motion_3d_reg[:, :1, :]
+        motion_3d_reg = motion_3d_reg - motion_3d_reg[:, :1, :]                       # motion_3d: (T,17,3)    
+        motion_theta = torch.cat((motion_smpl_pose, motion_smpl_shape), -1)
+        motion_smpl_3d = {
+            'theta': motion_theta,       # smpl pose and shape
+            'kp_3d': motion_3d_reg,      # 3D keypoints
+            'verts': motion_verts,       # 3D mesh vertices
+        }
+        return motion_2d, motion_smpl_3d

lib/data/dataset_motion_2d.py

@@ -0,0 +1,148 @@
+import sys
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader
+import numpy as np
+import os
+import random
+import copy
+import json
+from collections import defaultdict
+from lib.utils.utils_data import crop_scale, flip_data, resample, split_clips
+
+def posetrack2h36m(x):
+    '''
+        Input: x (T x V x C)
+
+        PoseTrack keypoints = [ 'nose',
+                                'head_bottom',
+                                'head_top',
+                                'left_ear',
+                                'right_ear',
+                                'left_shoulder',
+                                'right_shoulder',
+                                'left_elbow',
+                                'right_elbow',
+                                'left_wrist',
+                                'right_wrist',
+                                'left_hip',
+                                'right_hip',
+                                'left_knee',
+                                'right_knee',
+                                'left_ankle',
+                                'right_ankle']
+        H36M:
+        0: 'root',
+        1: 'rhip',
+        2: 'rkne',
+        3: 'rank',
+        4: 'lhip',
+        5: 'lkne',
+        6: 'lank',
+        7: 'belly',
+        8: 'neck',
+        9: 'nose',
+        10: 'head',
+        11: 'lsho',
+        12: 'lelb',
+        13: 'lwri',
+        14: 'rsho',
+        15: 'relb',
+        16: 'rwri'
+    '''
+    y = np.zeros(x.shape)
+    y[:,0,:] = (x[:,11,:] + x[:,12,:]) * 0.5
+    y[:,1,:] = x[:,12,:]
+    y[:,2,:] = x[:,14,:]
+    y[:,3,:] = x[:,16,:]
+    y[:,4,:] = x[:,11,:]
+    y[:,5,:] = x[:,13,:]
+    y[:,6,:] = x[:,15,:]
+    y[:,8,:] = x[:,1,:]
+    y[:,7,:] = (y[:,0,:] + y[:,8,:]) * 0.5
+    y[:,9,:] = x[:,0,:]
+    y[:,10,:] = x[:,2,:]
+    y[:,11,:] = x[:,5,:]
+    y[:,12,:] = x[:,7,:]
+    y[:,13,:] = x[:,9,:]
+    y[:,14,:] = x[:,6,:]
+    y[:,15,:] = x[:,8,:]
+    y[:,16,:] = x[:,10,:]
+    y[:,0,2] = np.minimum(x[:,11,2], x[:,12,2])
+    y[:,7,2] = np.minimum(y[:,0,2], y[:,8,2])
+    return y
+
+
+class PoseTrackDataset2D(Dataset):
+    def __init__(self, flip=True, scale_range=[0.25, 1]):
+        super(PoseTrackDataset2D, self).__init__()
+        self.flip = flip
+        data_root = "data/motion2d/posetrack18_annotations/train/"
+        file_list = sorted(os.listdir(data_root))
+        all_motions = []
+        all_motions_filtered = []
+        self.scale_range = scale_range
+        for filename in file_list:
+            with open(os.path.join(data_root, filename), 'r') as file:
+                json_dict = json.load(file)
+                annots = json_dict['annotations']
+                imgs = json_dict['images']
+                motions = defaultdict(list)
+                for annot in annots:
+                    tid = annot['track_id']
+                    pose2d = np.array(annot['keypoints']).reshape(-1,3)
+                    motions[tid].append(pose2d)
+            all_motions += list(motions.values())
+        for motion in all_motions:
+            if len(motion)<30:
+                continue
+            motion = np.array(motion[:30])
+            if np.sum(motion[:,:,2]) <= 306:  # Valid joint num threshold
+                continue
+            motion = crop_scale(motion, self.scale_range) 
+            motion = posetrack2h36m(motion)
+            motion[motion[:,:,2]==0] = 0
+            if np.sum(motion[:,0,2]) < 30:
+                continue                      # Root all visible (needed for framewise rootrel)
+            all_motions_filtered.append(motion)
+        all_motions_filtered = np.array(all_motions_filtered)
+        self.motions_2d = all_motions_filtered
+        
+    def __len__(self):
+        'Denotes the total number of samples'
+        return len(self.motions_2d)
+
+    def __getitem__(self, index):
+        'Generates one sample of data'
+        motion_2d = torch.FloatTensor(self.motions_2d[index])
+        if self.flip and random.random()>0.5:                       
+            motion_2d = flip_data(motion_2d)
+        return motion_2d, motion_2d
+    
+class InstaVDataset2D(Dataset):
+    def __init__(self, n_frames=81, data_stride=27, flip=True, valid_threshold=0.0, scale_range=[0.25, 1]):
+        super(InstaVDataset2D, self).__init__()
+        self.flip = flip
+        self.scale_range = scale_range
+        motion_all = np.load('data/motion2d/InstaVariety/motion_all.npy')
+        id_all = np.load('data/motion2d/InstaVariety/id_all.npy')
+        split_id = split_clips(id_all, n_frames, data_stride)  
+        motions_2d = motion_all[split_id]                        # [N, T, 17, 3]
+        valid_idx = (motions_2d[:,0,0,2] > valid_threshold)
+        self.motions_2d = motions_2d[valid_idx]
+        
+    def __len__(self):
+        'Denotes the total number of samples'
+        return len(self.motions_2d)
+
+    def __getitem__(self, index):
+        'Generates one sample of data'
+        motion_2d = self.motions_2d[index]
+        motion_2d = crop_scale(motion_2d, self.scale_range) 
+        motion_2d[motion_2d[:,:,2]==0] = 0
+        if self.flip and random.random()>0.5:                       
+            motion_2d = flip_data(motion_2d)
+        motion_2d = torch.FloatTensor(motion_2d)
+        return motion_2d, motion_2d
+        

lib/data/dataset_motion_3d.py

@@ -0,0 +1,68 @@
+import torch
+import numpy as np
+import glob
+import os
+import io
+import random
+import pickle
+from torch.utils.data import Dataset, DataLoader
+from lib.data.augmentation import Augmenter3D
+from lib.utils.tools import read_pkl
+from lib.utils.utils_data import flip_data
+    
+class MotionDataset(Dataset):
+    def __init__(self, args, subset_list, data_split): # data_split: train/test
+        np.random.seed(0)
+        self.data_root = args.data_root
+        self.subset_list = subset_list
+        self.data_split = data_split
+        file_list_all = []
+        for subset in self.subset_list:
+            data_path = os.path.join(self.data_root, subset, self.data_split)
+            motion_list = sorted(os.listdir(data_path))
+            for i in motion_list:
+                file_list_all.append(os.path.join(data_path, i))
+        self.file_list = file_list_all
+        
+    def __len__(self):
+        'Denotes the total number of samples'
+        return len(self.file_list)
+
+    def __getitem__(self, index):
+        raise NotImplementedError 
+
+class MotionDataset3D(MotionDataset):
+    def __init__(self, args, subset_list, data_split):
+        super(MotionDataset3D, self).__init__(args, subset_list, data_split)
+        self.flip = args.flip
+        self.synthetic = args.synthetic
+        self.aug = Augmenter3D(args)
+        self.gt_2d = args.gt_2d
+
+    def __getitem__(self, index):
+        'Generates one sample of data'
+        # Select sample
+        file_path = self.file_list[index]
+        motion_file = read_pkl(file_path)
+        motion_3d = motion_file["data_label"]  
+        if self.data_split=="train":
+            if self.synthetic or self.gt_2d:
+                motion_3d = self.aug.augment3D(motion_3d)
+                motion_2d = np.zeros(motion_3d.shape, dtype=np.float32)
+                motion_2d[:,:,:2] = motion_3d[:,:,:2]
+                motion_2d[:,:,2] = 1                        # No 2D detection, use GT xy and c=1.
+            elif motion_file["data_input"] is not None:     # Have 2D detection 
+                motion_2d = motion_file["data_input"]
+                if self.flip and random.random() > 0.5:                        # Training augmentation - random flipping
+                    motion_2d = flip_data(motion_2d)
+                    motion_3d = flip_data(motion_3d)
+            else:
+                raise ValueError('Training illegal.') 
+        elif self.data_split=="test":                                           
+            motion_2d = motion_file["data_input"]
+            if self.gt_2d:
+                motion_2d[:,:,:2] = motion_3d[:,:,:2]
+                motion_2d[:,:,2] = 1
+        else:
+            raise ValueError('Data split unknown.')    
+        return torch.FloatTensor(motion_2d), torch.FloatTensor(motion_3d)

lib/data/dataset_wild.py

@@ -0,0 +1,102 @@
+import torch
+import numpy as np
+import ipdb
+import glob
+import os
+import io
+import math
+import random
+import json
+import pickle
+import math
+from torch.utils.data import Dataset, DataLoader
+from lib.utils.utils_data import crop_scale
+
+def halpe2h36m(x):
+    '''
+        Input: x (T x V x C)  
+       //Halpe 26 body keypoints
+    {0,  "Nose"},
+    {1,  "LEye"},
+    {2,  "REye"},
+    {3,  "LEar"},
+    {4,  "REar"},
+    {5,  "LShoulder"},
+    {6,  "RShoulder"},
+    {7,  "LElbow"},
+    {8,  "RElbow"},
+    {9,  "LWrist"},
+    {10, "RWrist"},
+    {11, "LHip"},
+    {12, "RHip"},
+    {13, "LKnee"},
+    {14, "Rknee"},
+    {15, "LAnkle"},
+    {16, "RAnkle"},
+    {17,  "Head"},
+    {18,  "Neck"},
+    {19,  "Hip"},
+    {20, "LBigToe"},
+    {21, "RBigToe"},
+    {22, "LSmallToe"},
+    {23, "RSmallToe"},
+    {24, "LHeel"},
+    {25, "RHeel"},
+    '''
+    T, V, C = x.shape
+    y = np.zeros([T,17,C])
+    y[:,0,:] = x[:,19,:]
+    y[:,1,:] = x[:,12,:]
+    y[:,2,:] = x[:,14,:]
+    y[:,3,:] = x[:,16,:]
+    y[:,4,:] = x[:,11,:]
+    y[:,5,:] = x[:,13,:]
+    y[:,6,:] = x[:,15,:]
+    y[:,7,:] = (x[:,18,:] + x[:,19,:]) * 0.5
+    y[:,8,:] = x[:,18,:]
+    y[:,9,:] = x[:,0,:]
+    y[:,10,:] = x[:,17,:]
+    y[:,11,:] = x[:,5,:]
+    y[:,12,:] = x[:,7,:]
+    y[:,13,:] = x[:,9,:]
+    y[:,14,:] = x[:,6,:]
+    y[:,15,:] = x[:,8,:]
+    y[:,16,:] = x[:,10,:]
+    return y
+    
+def read_input(json_path, vid_size, scale_range, focus):
+    with open(json_path, "r") as read_file:
+        results = json.load(read_file)
+    kpts_all = []
+    for item in results:
+        if focus!=None and item['idx']!=focus:
+            continue
+        kpts = np.array(item['keypoints']).reshape([-1,3])
+        kpts_all.append(kpts)
+    kpts_all = np.array(kpts_all)
+    kpts_all = halpe2h36m(kpts_all)
+    if vid_size:
+        w, h = vid_size
+        scale = min(w,h) / 2.0
+        kpts_all[:,:,:2] = kpts_all[:,:,:2] - np.array([w, h]) / 2.0
+        kpts_all[:,:,:2] = kpts_all[:,:,:2] / scale
+        motion = kpts_all
+    if scale_range:
+        motion = crop_scale(kpts_all, scale_range) 
+    return motion.astype(np.float32)
+
+class WildDetDataset(Dataset):
+    def __init__(self, json_path, clip_len=243, vid_size=None, scale_range=None, focus=None):
+        self.json_path = json_path
+        self.clip_len = clip_len
+        self.vid_all = read_input(json_path, vid_size, scale_range, focus)
+        
+    def __len__(self):
+        'Denotes the total number of samples'
+        return math.ceil(len(self.vid_all) / self.clip_len)
+    
+    def __getitem__(self, index):
+        'Generates one sample of data'
+        st = index*self.clip_len
+        end = min((index+1)*self.clip_len, len(self.vid_all))
+        return self.vid_all[st:end]

lib/model/DSTformer.py

@@ -0,0 +1,362 @@
+import torch
+import torch.nn as nn
+import math
+import warnings
+import random
+import numpy as np
+from collections import OrderedDict
+from functools import partial
+from itertools import repeat
+from lib.model.drop import DropPath
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+    # Cut & paste from PyTorch official master until it's in a few official releases - RW
+    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+    def norm_cdf(x):
+        # Computes standard normal cumulative distribution function
+        return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+    if (mean < a - 2 * std) or (mean > b + 2 * std):
+        warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+                      "The distribution of values may be incorrect.",
+                      stacklevel=2)
+
+    with torch.no_grad():
+        # Values are generated by using a truncated uniform distribution and
+        # then using the inverse CDF for the normal distribution.
+        # Get upper and lower cdf values
+        l = norm_cdf((a - mean) / std)
+        u = norm_cdf((b - mean) / std)
+
+        # Uniformly fill tensor with values from [l, u], then translate to
+        # [2l-1, 2u-1].
+        tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+        # Use inverse cdf transform for normal distribution to get truncated
+        # standard normal
+        tensor.erfinv_()
+
+        # Transform to proper mean, std
+        tensor.mul_(std * math.sqrt(2.))
+        tensor.add_(mean)
+
+        # Clamp to ensure it's in the proper range
+        tensor.clamp_(min=a, max=b)
+        return tensor
+
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+    # type: (Tensor, float, float, float, float) -> Tensor
+    r"""Fills the input Tensor with values drawn from a truncated
+    normal distribution. The values are effectively drawn from the
+    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+    with values outside :math:`[a, b]` redrawn until they are within
+    the bounds. The method used for generating the random values works
+    best when :math:`a \leq \text{mean} \leq b`.
+    Args:
+        tensor: an n-dimensional `torch.Tensor`
+        mean: the mean of the normal distribution
+        std: the standard deviation of the normal distribution
+        a: the minimum cutoff value
+        b: the maximum cutoff value
+    Examples:
+        >>> w = torch.empty(3, 5)
+        >>> nn.init.trunc_normal_(w)
+    """
+    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+
+class MLP(nn.Module):
+    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class Attention(nn.Module):
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0., st_mode='vanilla'):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.mode = st_mode
+        if self.mode == 'parallel':
+            self.ts_attn = nn.Linear(dim*2, dim*2)
+            self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        else:
+            self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        self.attn_count_s = None
+        self.attn_count_t = None
+
+    def forward(self, x, seqlen=1):
+        B, N, C = x.shape
+        
+        if self.mode == 'series':
+            qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+            q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+            x = self.forward_spatial(q, k, v)
+            qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+            q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+            x = self.forward_temporal(q, k, v, seqlen=seqlen)
+        elif self.mode == 'parallel':
+            qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+            q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+            x_t = self.forward_temporal(q, k, v, seqlen=seqlen)
+            x_s = self.forward_spatial(q, k, v)
+            
+            alpha = torch.cat([x_s, x_t], dim=-1)
+            alpha = alpha.mean(dim=1, keepdim=True)
+            alpha = self.ts_attn(alpha).reshape(B, 1, C, 2)
+            alpha = alpha.softmax(dim=-1)
+            x = x_t * alpha[:,:,:,1] + x_s * alpha[:,:,:,0]
+        elif self.mode == 'coupling':
+            qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+            q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+            x = self.forward_coupling(q, k, v, seqlen=seqlen)
+        elif self.mode == 'vanilla':
+            qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+            q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+            x = self.forward_spatial(q, k, v)
+        elif self.mode == 'temporal':
+            qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+            q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+            x = self.forward_temporal(q, k, v, seqlen=seqlen)
+        elif self.mode == 'spatial':
+            qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+            q, k, v = qkv[0], qkv[1], qkv[2]   # make torchscript happy (cannot use tensor as tuple)
+            x = self.forward_spatial(q, k, v)
+        else:
+            raise NotImplementedError(self.mode)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+    
+    def reshape_T(self, x, seqlen=1, inverse=False):
+        if not inverse:
+            N, C = x.shape[-2:]
+            x = x.reshape(-1, seqlen, self.num_heads, N, C).transpose(1,2)
+            x = x.reshape(-1, self.num_heads, seqlen*N, C) #(B, H, TN, c)
+        else:
+            TN, C = x.shape[-2:]
+            x = x.reshape(-1, self.num_heads, seqlen, TN // seqlen, C).transpose(1,2)
+            x = x.reshape(-1, self.num_heads, TN // seqlen, C) #(BT, H, N, C)
+        return x 
+
+    def forward_coupling(self, q, k, v, seqlen=8):
+        BT, _, N, C = q.shape
+        q = self.reshape_T(q, seqlen)
+        k = self.reshape_T(k, seqlen)
+        v = self.reshape_T(v, seqlen)
+
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = attn @ v
+        x = self.reshape_T(x, seqlen, inverse=True)
+        x = x.transpose(1,2).reshape(BT, N, C*self.num_heads)
+        return x
+
+    def forward_spatial(self, q, k, v):
+        B, _, N, C = q.shape
+        attn = (q @ k.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = attn @ v
+        x = x.transpose(1,2).reshape(B, N, C*self.num_heads)
+        return x
+        
+    def forward_temporal(self, q, k, v, seqlen=8):
+        B, _, N, C = q.shape
+        qt = q.reshape(-1, seqlen, self.num_heads, N, C).permute(0, 2, 3, 1, 4) #(B, H, N, T, C)
+        kt = k.reshape(-1, seqlen, self.num_heads, N, C).permute(0, 2, 3, 1, 4) #(B, H, N, T, C)
+        vt = v.reshape(-1, seqlen, self.num_heads, N, C).permute(0, 2, 3, 1, 4) #(B, H, N, T, C)
+
+        attn = (qt @ kt.transpose(-2, -1)) * self.scale
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x = attn @ vt #(B, H, N, T, C)
+        x = x.permute(0, 3, 2, 1, 4).reshape(B, N, C*self.num_heads)
+        return x
+
+    def count_attn(self, attn):
+        attn = attn.detach().cpu().numpy()
+        attn = attn.mean(axis=1)
+        attn_t = attn[:, :, 1].mean(axis=1)
+        attn_s = attn[:, :, 0].mean(axis=1)
+        if self.attn_count_s is None:
+            self.attn_count_s = attn_s
+            self.attn_count_t = attn_t
+        else:
+            self.attn_count_s = np.concatenate([self.attn_count_s, attn_s], axis=0)
+            self.attn_count_t = np.concatenate([self.attn_count_t, attn_t], axis=0)
+
+class Block(nn.Module):
+
+    def __init__(self, dim, num_heads, mlp_ratio=4., mlp_out_ratio=1., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+                 drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, st_mode='stage_st', att_fuse=False):
+        super().__init__()
+        # assert 'stage' in st_mode
+        self.st_mode = st_mode
+        self.norm1_s = norm_layer(dim)
+        self.norm1_t = norm_layer(dim)
+        self.attn_s = Attention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, st_mode="spatial")
+        self.attn_t = Attention(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, st_mode="temporal")
+        
+        # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2_s = norm_layer(dim)
+        self.norm2_t = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        mlp_out_dim = int(dim * mlp_out_ratio)
+        self.mlp_s = MLP(in_features=dim, hidden_features=mlp_hidden_dim, out_features=mlp_out_dim, act_layer=act_layer, drop=drop)
+        self.mlp_t = MLP(in_features=dim, hidden_features=mlp_hidden_dim, out_features=mlp_out_dim, act_layer=act_layer, drop=drop)
+        self.att_fuse = att_fuse
+        if self.att_fuse:
+            self.ts_attn = nn.Linear(dim*2, dim*2)
+    def forward(self, x, seqlen=1):
+        if self.st_mode=='stage_st':
+            x = x + self.drop_path(self.attn_s(self.norm1_s(x), seqlen))
+            x = x + self.drop_path(self.mlp_s(self.norm2_s(x)))
+            x = x + self.drop_path(self.attn_t(self.norm1_t(x), seqlen))
+            x = x + self.drop_path(self.mlp_t(self.norm2_t(x)))
+        elif self.st_mode=='stage_ts':
+            x = x + self.drop_path(self.attn_t(self.norm1_t(x), seqlen))
+            x = x + self.drop_path(self.mlp_t(self.norm2_t(x)))
+            x = x + self.drop_path(self.attn_s(self.norm1_s(x), seqlen))
+            x = x + self.drop_path(self.mlp_s(self.norm2_s(x)))
+        elif self.st_mode=='stage_para':
+            x_t = x + self.drop_path(self.attn_t(self.norm1_t(x), seqlen))
+            x_t = x_t + self.drop_path(self.mlp_t(self.norm2_t(x_t)))
+            x_s = x + self.drop_path(self.attn_s(self.norm1_s(x), seqlen))
+            x_s = x_s + self.drop_path(self.mlp_s(self.norm2_s(x_s)))
+            if self.att_fuse:
+                #             x_s, x_t: [BF, J, dim]
+                alpha = torch.cat([x_s, x_t], dim=-1)
+                BF, J = alpha.shape[:2]
+                # alpha = alpha.mean(dim=1, keepdim=True)
+                alpha = self.ts_attn(alpha).reshape(BF, J, -1, 2)
+                alpha = alpha.softmax(dim=-1)
+                x = x_t * alpha[:,:,:,1] + x_s * alpha[:,:,:,0]
+            else:
+                x = (x_s + x_t)*0.5
+        else:
+            raise NotImplementedError(self.st_mode)
+        return x
+    
+class DSTformer(nn.Module):
+    def __init__(self, dim_in=3, dim_out=3, dim_feat=256, dim_rep=512,
+                 depth=5, num_heads=8, mlp_ratio=4, 
+                 num_joints=17, maxlen=243, 
+                 qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0., drop_path_rate=0., norm_layer=nn.LayerNorm, att_fuse=True):
+        super().__init__()
+        self.dim_out = dim_out
+        self.dim_feat = dim_feat
+        self.joints_embed = nn.Linear(dim_in, dim_feat)
+        self.pos_drop = nn.Dropout(p=drop_rate)
+        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]  # stochastic depth decay rule
+        self.blocks_st = nn.ModuleList([
+            Block(
+                dim=dim_feat, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                st_mode="stage_st")
+            for i in range(depth)])
+        self.blocks_ts = nn.ModuleList([
+            Block(
+                dim=dim_feat, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, 
+                st_mode="stage_ts")
+            for i in range(depth)])
+        self.norm = norm_layer(dim_feat)
+        if dim_rep:
+            self.pre_logits = nn.Sequential(OrderedDict([
+                ('fc', nn.Linear(dim_feat, dim_rep)),
+                ('act', nn.Tanh())
+            ]))
+        else:
+            self.pre_logits = nn.Identity()
+        self.head = nn.Linear(dim_rep, dim_out) if dim_out > 0 else nn.Identity()            
+        self.temp_embed = nn.Parameter(torch.zeros(1, maxlen, 1, dim_feat))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_joints, dim_feat))
+        trunc_normal_(self.temp_embed, std=.02)
+        trunc_normal_(self.pos_embed, std=.02)
+        self.apply(self._init_weights)
+        self.att_fuse = att_fuse
+        if self.att_fuse:
+            self.ts_attn = nn.ModuleList([nn.Linear(dim_feat*2, 2) for i in range(depth)])
+            for i in range(depth):
+                self.ts_attn[i].weight.data.fill_(0)
+                self.ts_attn[i].bias.data.fill_(0.5)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight, std=.02)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.LayerNorm):
+            nn.init.constant_(m.bias, 0)
+            nn.init.constant_(m.weight, 1.0)
+
+    def get_classifier(self):
+        return self.head
+
+    def reset_classifier(self, dim_out, global_pool=''):
+        self.dim_out = dim_out
+        self.head = nn.Linear(self.dim_feat, dim_out) if dim_out > 0 else nn.Identity()
+
+    def forward(self, x, return_rep=False):   
+        B, F, J, C = x.shape
+        x = x.reshape(-1, J, C)
+        BF = x.shape[0]
+        x = self.joints_embed(x)
+        x = x + self.pos_embed
+        _, J, C = x.shape
+        x = x.reshape(-1, F, J, C) + self.temp_embed[:,:F,:,:]
+        x = x.reshape(BF, J, C)
+        x = self.pos_drop(x)
+        alphas = []
+        for idx, (blk_st, blk_ts) in enumerate(zip(self.blocks_st, self.blocks_ts)):
+            x_st = blk_st(x, F)
+            x_ts = blk_ts(x, F)
+            if self.att_fuse:
+                att = self.ts_attn[idx]
+                alpha = torch.cat([x_st, x_ts], dim=-1)
+                BF, J = alpha.shape[:2]
+                alpha = att(alpha)
+                alpha = alpha.softmax(dim=-1)
+                x = x_st * alpha[:,:,0:1] + x_ts * alpha[:,:,1:2]
+            else:
+                x = (x_st + x_ts)*0.5
+        x = self.norm(x)
+        x = x.reshape(B, F, J, -1)
+        x = self.pre_logits(x)         # [B, F, J, dim_feat]
+        if return_rep:
+            return x
+        x = self.head(x)
+        return x
+
+    def get_representation(self, x):
+        return self.forward(x, return_rep=True)
+    

lib/model/drop.py

@@ -0,0 +1,43 @@
+""" DropBlock, DropPath
+PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
+Papers:
+DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
+Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
+Code:
+DropBlock impl inspired by two Tensorflow impl that I liked:
+ - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
+ - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+def drop_path(x, drop_prob: float = 0., training: bool = False):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
+    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
+    'survival rate' as the argument.
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = 1 - drop_prob
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
+    random_tensor.floor_()  # binarize
+    output = x.div(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Module):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)

lib/model/loss.py

@@ -0,0 +1,204 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import torch.nn.functional as F
+
+# Numpy-based errors
+
+def mpjpe(predicted, target):
+    """
+    Mean per-joint position error (i.e. mean Euclidean distance),
+    often referred to as "Protocol #1" in many papers.
+    """
+    assert predicted.shape == target.shape
+    return np.mean(np.linalg.norm(predicted - target, axis=len(target.shape)-1), axis=1)
+
+def p_mpjpe(predicted, target):
+    """
+    Pose error: MPJPE after rigid alignment (scale, rotation, and translation),
+    often referred to as "Protocol #2" in many papers.
+    """
+    assert predicted.shape == target.shape
+    
+    muX = np.mean(target, axis=1, keepdims=True)
+    muY = np.mean(predicted, axis=1, keepdims=True)
+    
+    X0 = target - muX
+    Y0 = predicted - muY
+
+    normX = np.sqrt(np.sum(X0**2, axis=(1, 2), keepdims=True))
+    normY = np.sqrt(np.sum(Y0**2, axis=(1, 2), keepdims=True))
+    
+    X0 /= normX
+    Y0 /= normY
+
+    H = np.matmul(X0.transpose(0, 2, 1), Y0)
+    U, s, Vt = np.linalg.svd(H)
+    V = Vt.transpose(0, 2, 1)
+    R = np.matmul(V, U.transpose(0, 2, 1))
+
+    # Avoid improper rotations (reflections), i.e. rotations with det(R) = -1
+    sign_detR = np.sign(np.expand_dims(np.linalg.det(R), axis=1))
+    V[:, :, -1] *= sign_detR
+    s[:, -1] *= sign_detR.flatten()
+    R = np.matmul(V, U.transpose(0, 2, 1)) # Rotation
+    tr = np.expand_dims(np.sum(s, axis=1, keepdims=True), axis=2)
+    a = tr * normX / normY # Scale
+    t = muX - a*np.matmul(muY, R) # Translation
+    # Perform rigid transformation on the input
+    predicted_aligned = a*np.matmul(predicted, R) + t
+    # Return MPJPE
+    return np.mean(np.linalg.norm(predicted_aligned - target, axis=len(target.shape)-1), axis=1)
+
+
+# PyTorch-based errors (for losses)
+
+def loss_mpjpe(predicted, target):
+    """
+    Mean per-joint position error (i.e. mean Euclidean distance),
+    often referred to as "Protocol #1" in many papers.
+    """
+    assert predicted.shape == target.shape
+    return torch.mean(torch.norm(predicted - target, dim=len(target.shape)-1))
+    
+def weighted_mpjpe(predicted, target, w):
+    """
+    Weighted mean per-joint position error (i.e. mean Euclidean distance)
+    """
+    assert predicted.shape == target.shape
+    assert w.shape[0] == predicted.shape[0]
+    return torch.mean(w * torch.norm(predicted - target, dim=len(target.shape)-1))
+
+def loss_2d_weighted(predicted, target, conf):
+    assert predicted.shape == target.shape
+    predicted_2d = predicted[:,:,:,:2]
+    target_2d = target[:,:,:,:2]
+    diff = (predicted_2d - target_2d) * conf
+    return torch.mean(torch.norm(diff, dim=-1))
+    
+def n_mpjpe(predicted, target):
+    """
+    Normalized MPJPE (scale only), adapted from:
+    https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning/blob/master/losses/poses.py
+    """
+    assert predicted.shape == target.shape
+    norm_predicted = torch.mean(torch.sum(predicted**2, dim=3, keepdim=True), dim=2, keepdim=True)
+    norm_target = torch.mean(torch.sum(target*predicted, dim=3, keepdim=True), dim=2, keepdim=True)
+    scale = norm_target / norm_predicted
+    return loss_mpjpe(scale * predicted, target)
+
+def weighted_bonelen_loss(predict_3d_length, gt_3d_length):
+    loss_length = 0.001 * torch.pow(predict_3d_length - gt_3d_length, 2).mean()
+    return loss_length
+
+def weighted_boneratio_loss(predict_3d_length, gt_3d_length):
+    loss_length = 0.1 * torch.pow((predict_3d_length - gt_3d_length)/gt_3d_length, 2).mean()
+    return loss_length
+
+def get_limb_lens(x):
+    '''
+        Input: (N, T, 17, 3)
+        Output: (N, T, 16)
+    '''
+    limbs_id = [[0,1], [1,2], [2,3],
+         [0,4], [4,5], [5,6],
+         [0,7], [7,8], [8,9], [9,10],
+         [8,11], [11,12], [12,13],
+         [8,14], [14,15], [15,16]
+        ]
+    limbs = x[:,:,limbs_id,:]
+    limbs = limbs[:,:,:,0,:]-limbs[:,:,:,1,:]
+    limb_lens = torch.norm(limbs, dim=-1)
+    return limb_lens
+
+def loss_limb_var(x):
+    '''
+        Input: (N, T, 17, 3)
+    '''
+    if x.shape[1]<=1:
+        return torch.FloatTensor(1).fill_(0.)[0].to(x.device)
+    limb_lens = get_limb_lens(x)
+    limb_lens_var = torch.var(limb_lens, dim=1)
+    limb_loss_var = torch.mean(limb_lens_var)
+    return limb_loss_var
+
+def loss_limb_gt(x, gt):
+    '''
+        Input: (N, T, 17, 3), (N, T, 17, 3)
+    '''
+    limb_lens_x = get_limb_lens(x)
+    limb_lens_gt = get_limb_lens(gt) # (N, T, 16)
+    return nn.L1Loss()(limb_lens_x, limb_lens_gt)
+
+def loss_velocity(predicted, target):
+    """
+    Mean per-joint velocity error (i.e. mean Euclidean distance of the 1st derivative)
+    """
+    assert predicted.shape == target.shape
+    if predicted.shape[1]<=1:
+        return torch.FloatTensor(1).fill_(0.)[0].to(predicted.device)
+    velocity_predicted = predicted[:,1:] - predicted[:,:-1]
+    velocity_target = target[:,1:] - target[:,:-1]
+    return torch.mean(torch.norm(velocity_predicted - velocity_target, dim=-1))
+
+def loss_joint(predicted, target):
+    assert predicted.shape == target.shape
+    return nn.L1Loss()(predicted, target)
+
+def get_angles(x):
+    '''
+        Input: (N, T, 17, 3)
+        Output: (N, T, 16)
+    '''
+    limbs_id = [[0,1], [1,2], [2,3],
+         [0,4], [4,5], [5,6],
+         [0,7], [7,8], [8,9], [9,10],
+         [8,11], [11,12], [12,13],
+         [8,14], [14,15], [15,16]
+        ]
+    angle_id = [[ 0,  3],
+                [ 0,  6],
+                [ 3,  6],
+                [ 0,  1],
+                [ 1,  2],
+                [ 3,  4],
+                [ 4,  5],
+                [ 6,  7],
+                [ 7, 10],
+                [ 7, 13],
+                [ 8, 13],
+                [10, 13],
+                [ 7,  8],
+                [ 8,  9],
+                [10, 11],
+                [11, 12],
+                [13, 14],
+                [14, 15] ]
+    eps = 1e-7
+    limbs = x[:,:,limbs_id,:]
+    limbs = limbs[:,:,:,0,:]-limbs[:,:,:,1,:]
+    angles = limbs[:,:,angle_id,:]
+    angle_cos = F.cosine_similarity(angles[:,:,:,0,:], angles[:,:,:,1,:], dim=-1)
+    return torch.acos(angle_cos.clamp(-1+eps, 1-eps)) 
+
+def loss_angle(x, gt):
+    '''
+        Input: (N, T, 17, 3), (N, T, 17, 3)
+    '''
+    limb_angles_x = get_angles(x)
+    limb_angles_gt = get_angles(gt)
+    return nn.L1Loss()(limb_angles_x, limb_angles_gt)
+
+def loss_angle_velocity(x, gt):
+    """
+    Mean per-angle velocity error (i.e. mean Euclidean distance of the 1st derivative)
+    """
+    assert x.shape == gt.shape
+    if x.shape[1]<=1:
+        return torch.FloatTensor(1).fill_(0.)[0].to(x.device)
+    x_a = get_angles(x)
+    gt_a = get_angles(gt)
+    x_av = x_a[:,1:] - x_a[:,:-1]
+    gt_av = gt_a[:,1:] - gt_a[:,:-1]
+    return nn.L1Loss()(x_av, gt_av)
+

lib/model/loss_mesh.py

@@ -0,0 +1,68 @@
+import torch
+import torch.nn as nn
+import ipdb
+from lib.utils.utils_mesh import batch_rodrigues
+from lib.model.loss import *
+
+class MeshLoss(nn.Module):
+    def __init__(
+            self,
+            loss_type='MSE',
+            device='cuda',
+    ):
+        super(MeshLoss, self).__init__()
+        self.device = device
+        self.loss_type = loss_type
+        if loss_type == 'MSE': 
+            self.criterion_keypoints = nn.MSELoss(reduction='none').to(self.device)
+            self.criterion_regr = nn.MSELoss().to(self.device)
+        elif loss_type == 'L1': 
+            self.criterion_keypoints = nn.L1Loss(reduction='none').to(self.device)
+            self.criterion_regr = nn.L1Loss().to(self.device)
+
+    def forward(
+            self,
+            smpl_output,
+            data_gt,
+    ):
+        # to reduce time dimension
+        reduce = lambda x: x.reshape((x.shape[0] * x.shape[1],) + x.shape[2:])
+        data_3d_theta = reduce(data_gt['theta'])
+
+        preds = smpl_output[-1]
+        pred_theta = preds['theta']
+        theta_size = pred_theta.shape[:2]
+        pred_theta = reduce(pred_theta)
+        preds_local = preds['kp_3d'] - preds['kp_3d'][:, :, 0:1,:]  # (N, T, 17, 3)
+        gt_local = data_gt['kp_3d'] - data_gt['kp_3d'][:, :, 0:1,:]
+        real_shape, pred_shape = data_3d_theta[:, 72:], pred_theta[:, 72:]
+        real_pose, pred_pose = data_3d_theta[:, :72], pred_theta[:, :72]
+        loss_dict = {}
+        loss_dict['loss_3d_pos'] = loss_mpjpe(preds_local, gt_local)
+        loss_dict['loss_3d_scale'] = n_mpjpe(preds_local, gt_local)
+        loss_dict['loss_3d_velocity'] = loss_velocity(preds_local, gt_local)
+        loss_dict['loss_lv'] = loss_limb_var(preds_local)
+        loss_dict['loss_lg'] = loss_limb_gt(preds_local, gt_local)
+        loss_dict['loss_a'] = loss_angle(preds_local, gt_local)
+        loss_dict['loss_av'] = loss_angle_velocity(preds_local, gt_local)
+        
+        if pred_theta.shape[0] > 0:
+            loss_pose, loss_shape = self.smpl_losses(pred_pose, pred_shape, real_pose, real_shape)
+            loss_norm = torch.norm(pred_theta, dim=-1).mean()
+            loss_dict['loss_shape'] = loss_shape 
+            loss_dict['loss_pose'] = loss_pose 
+            loss_dict['loss_norm'] = loss_norm 
+        return loss_dict
+        
+    def smpl_losses(self, pred_rotmat, pred_betas, gt_pose, gt_betas):
+        pred_rotmat_valid = batch_rodrigues(pred_rotmat.reshape(-1,3)).reshape(-1, 24, 3, 3)
+        gt_rotmat_valid = batch_rodrigues(gt_pose.reshape(-1,3)).reshape(-1, 24, 3, 3)
+        pred_betas_valid = pred_betas
+        gt_betas_valid = gt_betas
+        if len(pred_rotmat_valid) > 0:
+            loss_regr_pose = self.criterion_regr(pred_rotmat_valid, gt_rotmat_valid)
+            loss_regr_betas = self.criterion_regr(pred_betas_valid, gt_betas_valid)
+        else:
+            loss_regr_pose = torch.FloatTensor(1).fill_(0.).to(self.device)
+            loss_regr_betas = torch.FloatTensor(1).fill_(0.).to(self.device)
+        return loss_regr_pose, loss_regr_betas

lib/model/loss_supcon.py

@@ -0,0 +1,98 @@
+"""
+Author: Yonglong Tian ([email protected])
+Date: May 07, 2020
+"""
+from __future__ import print_function
+
+import torch
+import torch.nn as nn
+
+
+class SupConLoss(nn.Module):
+    """Supervised Contrastive Learning: https://arxiv.org/pdf/2004.11362.pdf.
+    It also supports the unsupervised contrastive loss in SimCLR"""
+    def __init__(self, temperature=0.07, contrast_mode='all',
+                 base_temperature=0.07):
+        super(SupConLoss, self).__init__()
+        self.temperature = temperature
+        self.contrast_mode = contrast_mode
+        self.base_temperature = base_temperature
+
+    def forward(self, features, labels=None, mask=None):
+        """Compute loss for model. If both `labels` and `mask` are None,
+        it degenerates to SimCLR unsupervised loss:
+        https://arxiv.org/pdf/2002.05709.pdf
+
+        Args:
+            features: hidden vector of shape [bsz, n_views, ...].
+            labels: ground truth of shape [bsz].
+            mask: contrastive mask of shape [bsz, bsz], mask_{i,j}=1 if sample j
+                has the same class as sample i. Can be asymmetric.
+        Returns:
+            A loss scalar.
+        """
+        device = (torch.device('cuda')
+                  if features.is_cuda
+                  else torch.device('cpu'))
+
+        if len(features.shape) < 3:
+            raise ValueError('`features` needs to be [bsz, n_views, ...],'
+                             'at least 3 dimensions are required')
+        if len(features.shape) > 3:
+            features = features.view(features.shape[0], features.shape[1], -1)
+
+        batch_size = features.shape[0]
+        if labels is not None and mask is not None:
+            raise ValueError('Cannot define both `labels` and `mask`')
+        elif labels is None and mask is None:
+            mask = torch.eye(batch_size, dtype=torch.float32).to(device)
+        elif labels is not None:
+            labels = labels.contiguous().view(-1, 1)
+            if labels.shape[0] != batch_size:
+                raise ValueError('Num of labels does not match num of features')
+            mask = torch.eq(labels, labels.T).float().to(device)
+        else:
+            mask = mask.float().to(device)
+
+        contrast_count = features.shape[1]
+        contrast_feature = torch.cat(torch.unbind(features, dim=1), dim=0)
+        if self.contrast_mode == 'one':
+            anchor_feature = features[:, 0]
+            anchor_count = 1
+        elif self.contrast_mode == 'all':
+            anchor_feature = contrast_feature
+            anchor_count = contrast_count
+        else:
+            raise ValueError('Unknown mode: {}'.format(self.contrast_mode))
+
+        # compute logits
+        anchor_dot_contrast = torch.div(
+            torch.matmul(anchor_feature, contrast_feature.T),
+            self.temperature)
+        # for numerical stability
+        logits_max, _ = torch.max(anchor_dot_contrast, dim=1, keepdim=True)
+        logits = anchor_dot_contrast - logits_max.detach()
+
+        # tile mask
+        mask = mask.repeat(anchor_count, contrast_count)
+        # mask-out self-contrast cases
+        logits_mask = torch.scatter(
+            torch.ones_like(mask),
+            1,
+            torch.arange(batch_size * anchor_count).view(-1, 1).to(device),
+            0
+        )
+        mask = mask * logits_mask
+
+        # compute log_prob
+        exp_logits = torch.exp(logits) * logits_mask
+        log_prob = logits - torch.log(exp_logits.sum(1, keepdim=True))
+
+        # compute mean of log-likelihood over positive
+        mean_log_prob_pos = (mask * log_prob).sum(1) / mask.sum(1)
+
+        # loss
+        loss = - (self.temperature / self.base_temperature) * mean_log_prob_pos
+        loss = loss.view(anchor_count, batch_size).mean()
+
+        return loss

lib/model/model_action.py

@@ -0,0 +1,71 @@
+import sys
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+    
+class ActionHeadClassification(nn.Module):
+    def __init__(self, dropout_ratio=0., dim_rep=512, num_classes=60, num_joints=17, hidden_dim=2048):
+        super(ActionHeadClassification, self).__init__()
+        self.dropout = nn.Dropout(p=dropout_ratio)
+        self.bn = nn.BatchNorm1d(hidden_dim, momentum=0.1)
+        self.relu = nn.ReLU(inplace=True)
+        self.fc1 = nn.Linear(dim_rep*num_joints, hidden_dim)
+        self.fc2 = nn.Linear(hidden_dim, num_classes)
+        
+    def forward(self, feat):
+        '''
+            Input: (N, M, T, J, C)
+        '''
+        N, M, T, J, C = feat.shape
+        feat = self.dropout(feat)
+        feat = feat.permute(0, 1, 3, 4, 2)      # (N, M, T, J, C) -> (N, M, J, C, T)
+        feat = feat.mean(dim=-1)
+        feat = feat.reshape(N, M, -1)           # (N, M, J*C)
+        feat = feat.mean(dim=1)
+        feat = self.fc1(feat)
+        feat = self.bn(feat)
+        feat = self.relu(feat)    
+        feat = self.fc2(feat)
+        return feat
+        
+class ActionHeadEmbed(nn.Module):
+    def __init__(self, dropout_ratio=0., dim_rep=512, num_joints=17, hidden_dim=2048):
+        super(ActionHeadEmbed, self).__init__()
+        self.dropout = nn.Dropout(p=dropout_ratio)
+        self.fc1 = nn.Linear(dim_rep*num_joints, hidden_dim)
+    def forward(self, feat):
+        '''
+            Input: (N, M, T, J, C)
+        '''
+        N, M, T, J, C = feat.shape
+        feat = self.dropout(feat)
+        feat = feat.permute(0, 1, 3, 4, 2)      # (N, M, T, J, C) -> (N, M, J, C, T)
+        feat = feat.mean(dim=-1)
+        feat = feat.reshape(N, M, -1)           # (N, M, J*C)
+        feat = feat.mean(dim=1)
+        feat = self.fc1(feat)
+        feat = F.normalize(feat, dim=-1)
+        return feat
+
+class ActionNet(nn.Module):
+    def __init__(self, backbone, dim_rep=512, num_classes=60, dropout_ratio=0., version='class', hidden_dim=2048, num_joints=17):
+        super(ActionNet, self).__init__()
+        self.backbone = backbone
+        self.feat_J = num_joints
+        if version=='class':
+            self.head = ActionHeadClassification(dropout_ratio=dropout_ratio, dim_rep=dim_rep, num_classes=num_classes, num_joints=num_joints)
+        elif version=='embed':
+            self.head = ActionHeadEmbed(dropout_ratio=dropout_ratio, dim_rep=dim_rep, hidden_dim=hidden_dim, num_joints=num_joints)
+        else:
+            raise Exception('Version Error.')
+        
+    def forward(self, x):
+        '''
+            Input: (N, M x T x 17 x 3) 
+        '''
+        N, M, T, J, C = x.shape
+        x = x.reshape(N*M, T, J, C)        
+        feat = self.backbone.get_representation(x)
+        feat = feat.reshape([N, M, T, self.feat_J, -1])      # (N, M, T, J, C)
+        out = self.head(feat)
+        return out

lib/model/model_mesh.py

@@ -0,0 +1,101 @@
+import sys
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+from lib.utils.utils_smpl import SMPL
+from lib.utils.utils_mesh import rotation_matrix_to_angle_axis, rot6d_to_rotmat
+
+class SMPLRegressor(nn.Module):
+    def __init__(self, args, dim_rep=512, num_joints=17, hidden_dim=2048, dropout_ratio=0.):
+        super(SMPLRegressor, self).__init__()
+        param_pose_dim = 24 * 6
+        self.dropout = nn.Dropout(p=dropout_ratio)
+        self.fc1 = nn.Linear(num_joints*dim_rep, hidden_dim)
+        self.pool2 = nn.AdaptiveAvgPool2d((None, 1))
+        self.fc2 = nn.Linear(num_joints*dim_rep, hidden_dim)
+        self.bn1 = nn.BatchNorm1d(hidden_dim, momentum=0.1)
+        self.bn2 = nn.BatchNorm1d(hidden_dim, momentum=0.1)
+        self.relu1 = nn.ReLU(inplace=True)
+        self.relu2 = nn.ReLU(inplace=True)
+        self.head_pose = nn.Linear(hidden_dim, param_pose_dim)
+        self.head_shape = nn.Linear(hidden_dim, 10)
+        nn.init.xavier_uniform_(self.head_pose.weight, gain=0.01)
+        nn.init.xavier_uniform_(self.head_shape.weight, gain=0.01)
+        self.smpl = SMPL(
+            args.data_root,
+            batch_size=64,
+            create_transl=False,
+        )
+        mean_params = np.load(self.smpl.smpl_mean_params)
+        init_pose = torch.from_numpy(mean_params['pose'][:]).unsqueeze(0)
+        init_shape = torch.from_numpy(mean_params['shape'][:].astype('float32')).unsqueeze(0)
+        self.register_buffer('init_pose', init_pose)
+        self.register_buffer('init_shape', init_shape)
+        self.J_regressor = self.smpl.J_regressor_h36m
+
+    def forward(self, feat, init_pose=None, init_shape=None):
+        N, T, J, C = feat.shape
+        NT = N * T
+        feat = feat.reshape(N, T, -1)
+
+        feat_pose = feat.reshape(NT, -1)     # (N*T, J*C)
+
+        feat_pose = self.dropout(feat_pose)
+        feat_pose = self.fc1(feat_pose)
+        feat_pose = self.bn1(feat_pose)
+        feat_pose = self.relu1(feat_pose)    # (NT, C)
+
+        feat_shape = feat.permute(0,2,1)     # (N, T, J*C) -> (N, J*C, T)
+        feat_shape = self.pool2(feat_shape).reshape(N, -1)          # (N, J*C)
+
+        feat_shape = self.dropout(feat_shape)
+        feat_shape = self.fc2(feat_shape)
+        feat_shape = self.bn2(feat_shape)
+        feat_shape = self.relu2(feat_shape)     # (N, C)
+
+        pred_pose = self.init_pose.expand(NT, -1)   # (NT, C)
+        pred_shape = self.init_shape.expand(N, -1)  # (N, C)
+
+        pred_pose = self.head_pose(feat_pose) + pred_pose
+        pred_shape = self.head_shape(feat_shape) + pred_shape
+        pred_shape = pred_shape.expand(T, N, -1).permute(1, 0, 2).reshape(NT, -1)
+        pred_rotmat = rot6d_to_rotmat(pred_pose).view(-1, 24, 3, 3)
+        pred_output = self.smpl(
+            betas=pred_shape,
+            body_pose=pred_rotmat[:, 1:],
+            global_orient=pred_rotmat[:, 0].unsqueeze(1),
+            pose2rot=False
+        )
+        pred_vertices = pred_output.vertices*1000.0
+        assert self.J_regressor is not None
+        J_regressor_batch = self.J_regressor[None, :].expand(pred_vertices.shape[0], -1, -1).to(pred_vertices.device)
+        pred_joints = torch.matmul(J_regressor_batch, pred_vertices)
+        pose = rotation_matrix_to_angle_axis(pred_rotmat.reshape(-1, 3, 3)).reshape(-1, 72)
+        output = [{
+            'theta'  : torch.cat([pose, pred_shape], dim=1),    # (N*T, 72+10)
+            'verts'  : pred_vertices,                           # (N*T, 6890, 3)
+            'kp_3d'  : pred_joints,                             # (N*T, 17, 3)
+        }]
+        return output
+
+class MeshRegressor(nn.Module):
+    def __init__(self, args, backbone, dim_rep=512, num_joints=17, hidden_dim=2048, dropout_ratio=0.5):
+        super(MeshRegressor, self).__init__()
+        self.backbone = backbone
+        self.feat_J = num_joints
+        self.head = SMPLRegressor(args, dim_rep, num_joints, hidden_dim, dropout_ratio)
+        
+    def forward(self, x, init_pose=None, init_shape=None, n_iter=3):
+        '''
+            Input: (N x T x 17 x 3) 
+        '''
+        N, T, J, C = x.shape  
+        feat = self.backbone.get_representation(x)
+        feat = feat.reshape([N, T, self.feat_J, -1])      # (N, T, J, C)
+        smpl_output = self.head(feat)
+        for s in smpl_output:
+            s['theta'] = s['theta'].reshape(N, T, -1)
+            s['verts'] = s['verts'].reshape(N, T, -1, 3)
+            s['kp_3d'] = s['kp_3d'].reshape(N, T, -1, 3)
+        return smpl_output

lib/utils/learning.py

@@ -0,0 +1,102 @@
+import os
+import numpy as np
+import torch
+import torch.nn as nn
+from functools import partial
+from lib.model.DSTformer import DSTformer
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+        
+def accuracy(output, target, topk=(1,)):
+    """Computes the accuracy over the k top predictions for the specified values of k"""
+    with torch.no_grad():
+        maxk = max(topk)
+        batch_size = target.size(0)
+        _, pred = output.topk(maxk, 1, True, True)
+        pred = pred.t()
+        correct = pred.eq(target.view(1, -1).expand_as(pred))
+        res = []
+        for k in topk:
+            correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
+            res.append(correct_k.mul_(100.0 / batch_size))
+        return res
+
+def load_pretrained_weights(model, checkpoint):
+    """Load pretrianed weights to model
+    Incompatible layers (unmatched in name or size) will be ignored
+    Args:
+    - model (nn.Module): network model, which must not be nn.DataParallel
+    - weight_path (str): path to pretrained weights
+    """
+    import collections
+    if 'state_dict' in checkpoint:
+        state_dict = checkpoint['state_dict']
+    else:
+        state_dict = checkpoint
+    model_dict = model.state_dict()
+    new_state_dict = collections.OrderedDict()
+    matched_layers, discarded_layers = [], []
+    for k, v in state_dict.items():
+        # If the pretrained state_dict was saved as nn.DataParallel,
+        # keys would contain "module.", which should be ignored.
+        if k.startswith('module.'):
+            k = k[7:]
+        if k in model_dict and model_dict[k].size() == v.size():
+            new_state_dict[k] = v
+            matched_layers.append(k)
+        else:
+            discarded_layers.append(k)
+    model_dict.update(new_state_dict)
+    model.load_state_dict(model_dict, strict=True)
+    print('load_weight', len(matched_layers))
+    return model
+
+def partial_train_layers(model, partial_list):
+    """Train partial layers of a given model."""
+    for name, p in model.named_parameters():
+        p.requires_grad = False
+        for trainable in partial_list:
+            if trainable in name:
+                p.requires_grad = True
+                break
+    return model
+
+def load_backbone(args):
+    if not(hasattr(args, "backbone")):
+        args.backbone = 'DSTformer' # Default
+    if args.backbone=='DSTformer':
+        model_backbone = DSTformer(dim_in=3, dim_out=3, dim_feat=args.dim_feat, dim_rep=args.dim_rep, 
+                                   depth=args.depth, num_heads=args.num_heads, mlp_ratio=args.mlp_ratio, norm_layer=partial(nn.LayerNorm, eps=1e-6), 
+                                   maxlen=args.maxlen, num_joints=args.num_joints)
+    elif args.backbone=='TCN':
+        from lib.model.model_tcn import PoseTCN
+        model_backbone = PoseTCN()
+    elif args.backbone=='poseformer':
+        from lib.model.model_poseformer import PoseTransformer 
+        model_backbone = PoseTransformer(num_frame=args.maxlen, num_joints=args.num_joints, in_chans=3, embed_dim_ratio=32, depth=4,
+            num_heads=8, mlp_ratio=2., qkv_bias=True, qk_scale=None,drop_path_rate=0, attn_mask=None) 
+    elif args.backbone=='mixste':
+        from lib.model.model_mixste import MixSTE2 
+        model_backbone = MixSTE2(num_frame=args.maxlen, num_joints=args.num_joints, in_chans=3, embed_dim_ratio=512, depth=8,
+        num_heads=8, mlp_ratio=2., qkv_bias=True, qk_scale=None,drop_path_rate=0)
+    elif args.backbone=='stgcn':
+        from lib.model.model_stgcn import Model as STGCN 
+        model_backbone = STGCN()
+    else:
+        raise Exception("Undefined backbone type.")
+    return model_backbone

lib/utils/tools.py

@@ -0,0 +1,69 @@
+import numpy as np
+import os, sys
+import pickle
+import yaml
+from easydict import EasyDict as edict
+from typing import Any, IO
+
+ROOT_PATH = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..')
+
+class TextLogger:
+    def __init__(self, log_path):
+        self.log_path = log_path
+        with open(self.log_path, "w") as f:
+            f.write("")
+    def log(self, log):
+        with open(self.log_path, "a+") as f:
+            f.write(log + "\n")
+
+class Loader(yaml.SafeLoader):
+    """YAML Loader with `!include` constructor."""
+
+    def __init__(self, stream: IO) -> None:
+        """Initialise Loader."""
+
+        try:
+            self._root = os.path.split(stream.name)[0]
+        except AttributeError:
+            self._root = os.path.curdir
+
+        super().__init__(stream)
+
+def construct_include(loader: Loader, node: yaml.Node) -> Any:
+    """Include file referenced at node."""
+
+    filename = os.path.abspath(os.path.join(loader._root, loader.construct_scalar(node)))
+    extension = os.path.splitext(filename)[1].lstrip('.')
+
+    with open(filename, 'r') as f:
+        if extension in ('yaml', 'yml'):
+            return yaml.load(f, Loader)
+        elif extension in ('json', ):
+            return json.load(f)
+        else:
+            return ''.join(f.readlines())
+
+def get_config(config_path):
+    yaml.add_constructor('!include', construct_include, Loader)
+    with open(config_path, 'r') as stream:
+        config = yaml.load(stream, Loader=Loader)
+    config = edict(config)
+    _, config_filename = os.path.split(config_path)
+    config_name, _ = os.path.splitext(config_filename)
+    config.name = config_name
+    return config
+
+def ensure_dir(path):
+    """
+    create path by first checking its existence,
+    :param paths: path
+    :return:
+    """
+    if not os.path.exists(path):
+        os.makedirs(path)
+        
+def read_pkl(data_url):
+    file = open(data_url,'rb')
+    content = pickle.load(file)
+    file.close()
+    return content

lib/utils/utils_data.py

@@ -0,0 +1,112 @@
+import os
+import torch
+import torch.nn.functional as F
+import numpy as np
+import copy
+
+def crop_scale(motion, scale_range=[1, 1]):
+    '''
+        Motion: [(M), T, 17, 3].
+        Normalize to [-1, 1]
+    '''
+    result = copy.deepcopy(motion)
+    valid_coords = motion[motion[..., 2]!=0][:,:2]
+    if len(valid_coords) < 4:
+        return np.zeros(motion.shape)
+    xmin = min(valid_coords[:,0])
+    xmax = max(valid_coords[:,0])
+    ymin = min(valid_coords[:,1])
+    ymax = max(valid_coords[:,1])
+    ratio = np.random.uniform(low=scale_range[0], high=scale_range[1], size=1)[0]
+    scale = max(xmax-xmin, ymax-ymin) * ratio
+    if scale==0:
+        return np.zeros(motion.shape)
+    xs = (xmin+xmax-scale) / 2
+    ys = (ymin+ymax-scale) / 2
+    result[...,:2] = (motion[..., :2]- [xs,ys]) / scale
+    result[...,:2] = (result[..., :2] - 0.5) * 2
+    result = np.clip(result, -1, 1)
+    return result
+
+def crop_scale_3d(motion, scale_range=[1, 1]):
+    '''
+        Motion: [T, 17, 3]. (x, y, z)
+        Normalize to [-1, 1]
+        Z is relative to the first frame's root.
+    '''
+    result = copy.deepcopy(motion)
+    result[:,:,2] = result[:,:,2] - result[0,0,2]
+    xmin = np.min(motion[...,0])
+    xmax = np.max(motion[...,0])
+    ymin = np.min(motion[...,1])
+    ymax = np.max(motion[...,1])
+    ratio = np.random.uniform(low=scale_range[0], high=scale_range[1], size=1)[0]
+    scale = max(xmax-xmin, ymax-ymin) / ratio
+    if scale==0:
+        return np.zeros(motion.shape)
+    xs = (xmin+xmax-scale) / 2
+    ys = (ymin+ymax-scale) / 2
+    result[...,:2] = (motion[..., :2]- [xs,ys]) / scale
+    result[...,2] = result[...,2] / scale
+    result = (result - 0.5) * 2
+    return result
+
+def flip_data(data):
+    """
+    horizontal flip
+        data: [N, F, 17, D] or [F, 17, D]. X (horizontal coordinate) is the first channel in D.
+    Return
+        result: same
+    """
+    left_joints = [4, 5, 6, 11, 12, 13]
+    right_joints = [1, 2, 3, 14, 15, 16]
+    flipped_data = copy.deepcopy(data)
+    flipped_data[..., 0] *= -1                                               # flip x of all joints
+    flipped_data[..., left_joints+right_joints, :] = flipped_data[..., right_joints+left_joints, :]
+    return flipped_data
+
+def resample(ori_len, target_len, replay=False, randomness=True):
+    if replay:
+        if ori_len > target_len:
+            st = np.random.randint(ori_len-target_len)
+            return range(st, st+target_len)  # Random clipping from sequence
+        else:
+            return np.array(range(target_len)) % ori_len  # Replay padding
+    else:
+        if randomness:
+            even = np.linspace(0, ori_len, num=target_len, endpoint=False)
+            if ori_len < target_len:
+                low = np.floor(even)
+                high = np.ceil(even)
+                sel = np.random.randint(2, size=even.shape)
+                result = np.sort(sel*low+(1-sel)*high)
+            else:
+                interval = even[1] - even[0]
+                result = np.random.random(even.shape)*interval + even
+            result = np.clip(result, a_min=0, a_max=ori_len-1).astype(np.uint32)
+        else:
+            result = np.linspace(0, ori_len, num=target_len, endpoint=False, dtype=int)
+        return result
+
+def split_clips(vid_list, n_frames, data_stride):
+    result = []
+    n_clips = 0
+    st = 0
+    i = 0
+    saved = set()
+    while i<len(vid_list):
+        i += 1
+        if i-st == n_frames:
+            result.append(range(st,i))
+            saved.add(vid_list[i-1])
+            st = st + data_stride
+            n_clips += 1
+        if i==len(vid_list):
+            break
+        if vid_list[i]!=vid_list[i-1]: 
+            if not (vid_list[i-1] in saved):
+                resampled = resample(i-st, n_frames) + st
+                result.append(resampled)
+                saved.add(vid_list[i-1])
+            st = i
+    return result

lib/utils/utils_mesh.py

@@ -0,0 +1,521 @@
+import torch
+import numpy as np
+from torch.nn import functional as F
+import copy
+# from lib.utils.rotation_conversions import axis_angle_to_matrix, matrix_to_rotation_6d
+
+
+def batch_rodrigues(axisang):
+    # This function is borrowed from https://github.com/MandyMo/pytorch_HMR/blob/master/src/util.py#L37
+    # axisang N x 3
+    axisang_norm = torch.norm(axisang + 1e-8, p=2, dim=1)
+    angle = torch.unsqueeze(axisang_norm, -1)
+    axisang_normalized = torch.div(axisang, angle)
+    angle = angle * 0.5
+    v_cos = torch.cos(angle)
+    v_sin = torch.sin(angle)
+    quat = torch.cat([v_cos, v_sin * axisang_normalized], dim=1)
+    rot_mat = quat2mat(quat)
+    rot_mat = rot_mat.view(rot_mat.shape[0], 9)
+    return rot_mat
+
+
+def quat2mat(quat):
+    """
+    This function is borrowed from https://github.com/MandyMo/pytorch_HMR/blob/master/src/util.py#L50
+
+    Convert quaternion coefficients to rotation matrix.
+    Args:
+        quat: size = [batch_size, 4] 4 <===>(w, x, y, z)
+    Returns:
+        Rotation matrix corresponding to the quaternion -- size = [batch_size, 3, 3]
+    """
+    norm_quat = quat
+    norm_quat = norm_quat / norm_quat.norm(p=2, dim=1, keepdim=True)
+    w, x, y, z = norm_quat[:, 0], norm_quat[:, 1], norm_quat[:,
+                                                             2], norm_quat[:,
+                                                                           3]
+
+    batch_size = quat.size(0)
+
+    w2, x2, y2, z2 = w.pow(2), x.pow(2), y.pow(2), z.pow(2)
+    wx, wy, wz = w * x, w * y, w * z
+    xy, xz, yz = x * y, x * z, y * z
+
+    rotMat = torch.stack([
+        w2 + x2 - y2 - z2, 2 * xy - 2 * wz, 2 * wy + 2 * xz, 2 * wz + 2 * xy,
+        w2 - x2 + y2 - z2, 2 * yz - 2 * wx, 2 * xz - 2 * wy, 2 * wx + 2 * yz,
+        w2 - x2 - y2 + z2
+    ],
+                         dim=1).view(batch_size, 3, 3)
+    return rotMat
+
+
+def rotation_matrix_to_angle_axis(rotation_matrix):
+    """
+    This function is borrowed from https://github.com/kornia/kornia
+
+    Convert 3x4 rotation matrix to Rodrigues vector
+
+    Args:
+        rotation_matrix (Tensor): rotation matrix.
+
+    Returns:
+        Tensor: Rodrigues vector transformation.
+
+    Shape:
+        - Input: :math:`(N, 3, 4)`
+        - Output: :math:`(N, 3)`
+
+    Example:
+        >>> input = torch.rand(2, 3, 4)  # Nx4x4
+        >>> output = tgm.rotation_matrix_to_angle_axis(input)  # Nx3
+    """
+    if rotation_matrix.shape[1:] == (3,3):
+        rot_mat = rotation_matrix.reshape(-1, 3, 3)
+        hom = torch.tensor([0, 0, 1], dtype=torch.float32,
+                           device=rotation_matrix.device).reshape(1, 3, 1).expand(rot_mat.shape[0], -1, -1)
+        rotation_matrix = torch.cat([rot_mat, hom], dim=-1)
+
+    quaternion = rotation_matrix_to_quaternion(rotation_matrix)
+    aa = quaternion_to_angle_axis(quaternion)
+    aa[torch.isnan(aa)] = 0.0
+    return aa
+
+
+def quaternion_to_angle_axis(quaternion: torch.Tensor) -> torch.Tensor:
+    """
+    This function is borrowed from https://github.com/kornia/kornia
+
+    Convert quaternion vector to angle axis of rotation.
+
+    Adapted from ceres C++ library: ceres-solver/include/ceres/rotation.h
+
+    Args:
+        quaternion (torch.Tensor): tensor with quaternions.
+
+    Return:
+        torch.Tensor: tensor with angle axis of rotation.
+
+    Shape:
+        - Input: :math:`(*, 4)` where `*` means, any number of dimensions
+        - Output: :math:`(*, 3)`
+
+    Example:
+        >>> quaternion = torch.rand(2, 4)  # Nx4
+        >>> angle_axis = tgm.quaternion_to_angle_axis(quaternion)  # Nx3
+    """
+    if not torch.is_tensor(quaternion):
+        raise TypeError("Input type is not a torch.Tensor. Got {}".format(
+            type(quaternion)))
+
+    if not quaternion.shape[-1] == 4:
+        raise ValueError("Input must be a tensor of shape Nx4 or 4. Got {}"
+                         .format(quaternion.shape))
+    # unpack input and compute conversion
+    q1: torch.Tensor = quaternion[..., 1]
+    q2: torch.Tensor = quaternion[..., 2]
+    q3: torch.Tensor = quaternion[..., 3]
+    sin_squared_theta: torch.Tensor = q1 * q1 + q2 * q2 + q3 * q3
+
+    sin_theta: torch.Tensor = torch.sqrt(sin_squared_theta)
+    cos_theta: torch.Tensor = quaternion[..., 0]
+    two_theta: torch.Tensor = 2.0 * torch.where(
+        cos_theta < 0.0,
+        torch.atan2(-sin_theta, -cos_theta),
+        torch.atan2(sin_theta, cos_theta))
+
+    k_pos: torch.Tensor = two_theta / sin_theta
+    k_neg: torch.Tensor = 2.0 * torch.ones_like(sin_theta)
+    k: torch.Tensor = torch.where(sin_squared_theta > 0.0, k_pos, k_neg)
+
+    angle_axis: torch.Tensor = torch.zeros_like(quaternion)[..., :3]
+    angle_axis[..., 0] += q1 * k
+    angle_axis[..., 1] += q2 * k
+    angle_axis[..., 2] += q3 * k
+    return angle_axis
+
+
+def rotation_matrix_to_quaternion(rotation_matrix, eps=1e-6):
+    """
+    This function is borrowed from https://github.com/kornia/kornia
+
+    Convert 3x4 rotation matrix to 4d quaternion vector
+
+    This algorithm is based on algorithm described in
+    https://github.com/KieranWynn/pyquaternion/blob/master/pyquaternion/quaternion.py#L201
+
+    Args:
+        rotation_matrix (Tensor): the rotation matrix to convert.
+
+    Return:
+        Tensor: the rotation in quaternion
+
+    Shape:
+        - Input: :math:`(N, 3, 4)`
+        - Output: :math:`(N, 4)`
+
+    Example:
+        >>> input = torch.rand(4, 3, 4)  # Nx3x4
+        >>> output = tgm.rotation_matrix_to_quaternion(input)  # Nx4
+    """
+    if not torch.is_tensor(rotation_matrix):
+        raise TypeError("Input type is not a torch.Tensor. Got {}".format(
+            type(rotation_matrix)))
+
+    if len(rotation_matrix.shape) > 3:
+        raise ValueError(
+            "Input size must be a three dimensional tensor. Got {}".format(
+                rotation_matrix.shape))
+    if not rotation_matrix.shape[-2:] == (3, 4):
+        raise ValueError(
+            "Input size must be a N x 3 x 4  tensor. Got {}".format(
+                rotation_matrix.shape))
+
+    rmat_t = torch.transpose(rotation_matrix, 1, 2)
+
+    mask_d2 = rmat_t[:, 2, 2] < eps
+
+    mask_d0_d1 = rmat_t[:, 0, 0] > rmat_t[:, 1, 1]
+    mask_d0_nd1 = rmat_t[:, 0, 0] < -rmat_t[:, 1, 1]
+
+    t0 = 1 + rmat_t[:, 0, 0] - rmat_t[:, 1, 1] - rmat_t[:, 2, 2]
+    q0 = torch.stack([rmat_t[:, 1, 2] - rmat_t[:, 2, 1],
+                      t0, rmat_t[:, 0, 1] + rmat_t[:, 1, 0],
+                      rmat_t[:, 2, 0] + rmat_t[:, 0, 2]], -1)
+    t0_rep = t0.repeat(4, 1).t()
+
+    t1 = 1 - rmat_t[:, 0, 0] + rmat_t[:, 1, 1] - rmat_t[:, 2, 2]
+    q1 = torch.stack([rmat_t[:, 2, 0] - rmat_t[:, 0, 2],
+                      rmat_t[:, 0, 1] + rmat_t[:, 1, 0],
+                      t1, rmat_t[:, 1, 2] + rmat_t[:, 2, 1]], -1)
+    t1_rep = t1.repeat(4, 1).t()
+
+    t2 = 1 - rmat_t[:, 0, 0] - rmat_t[:, 1, 1] + rmat_t[:, 2, 2]
+    q2 = torch.stack([rmat_t[:, 0, 1] - rmat_t[:, 1, 0],
+                      rmat_t[:, 2, 0] + rmat_t[:, 0, 2],
+                      rmat_t[:, 1, 2] + rmat_t[:, 2, 1], t2], -1)
+    t2_rep = t2.repeat(4, 1).t()
+
+    t3 = 1 + rmat_t[:, 0, 0] + rmat_t[:, 1, 1] + rmat_t[:, 2, 2]
+    q3 = torch.stack([t3, rmat_t[:, 1, 2] - rmat_t[:, 2, 1],
+                      rmat_t[:, 2, 0] - rmat_t[:, 0, 2],
+                      rmat_t[:, 0, 1] - rmat_t[:, 1, 0]], -1)
+    t3_rep = t3.repeat(4, 1).t()
+
+    mask_c0 = mask_d2 * mask_d0_d1
+    mask_c1 = mask_d2 * ~mask_d0_d1
+    mask_c2 = ~mask_d2 * mask_d0_nd1
+    mask_c3 = ~mask_d2 * ~mask_d0_nd1
+    mask_c0 = mask_c0.view(-1, 1).type_as(q0)
+    mask_c1 = mask_c1.view(-1, 1).type_as(q1)
+    mask_c2 = mask_c2.view(-1, 1).type_as(q2)
+    mask_c3 = mask_c3.view(-1, 1).type_as(q3)
+
+    q = q0 * mask_c0 + q1 * mask_c1 + q2 * mask_c2 + q3 * mask_c3
+    q /= torch.sqrt(t0_rep * mask_c0 + t1_rep * mask_c1 +  # noqa
+                    t2_rep * mask_c2 + t3_rep * mask_c3)  # noqa
+    q *= 0.5
+    return q
+
+
+def estimate_translation_np(S, joints_2d, joints_conf, focal_length=5000., img_size=224.):
+    """
+    This function is borrowed from https://github.com/nkolot/SPIN/utils/geometry.py
+
+    Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d.
+    Input:
+        S: (25, 3) 3D joint locations
+        joints: (25, 3) 2D joint locations and confidence
+    Returns:
+        (3,) camera translation vector
+    """
+
+    num_joints = S.shape[0]
+    # focal length
+    f = np.array([focal_length,focal_length])
+    # optical center
+    center = np.array([img_size/2., img_size/2.])
+
+    # transformations
+    Z = np.reshape(np.tile(S[:,2],(2,1)).T,-1)
+    XY = np.reshape(S[:,0:2],-1)
+    O = np.tile(center,num_joints)
+    F = np.tile(f,num_joints)
+    weight2 = np.reshape(np.tile(np.sqrt(joints_conf),(2,1)).T,-1)
+
+    # least squares
+    Q = np.array([F*np.tile(np.array([1,0]),num_joints), F*np.tile(np.array([0,1]),num_joints), O-np.reshape(joints_2d,-1)]).T
+    c = (np.reshape(joints_2d,-1)-O)*Z - F*XY
+
+    # weighted least squares
+    W = np.diagflat(weight2)
+    Q = np.dot(W,Q)
+    c = np.dot(W,c)
+
+    # square matrix
+    A = np.dot(Q.T,Q)
+    b = np.dot(Q.T,c)
+
+    # solution
+    trans = np.linalg.solve(A, b)
+
+    return trans
+
+
+def estimate_translation(S, joints_2d, focal_length=5000., img_size=224.):
+    """
+    This function is borrowed from https://github.com/nkolot/SPIN/utils/geometry.py
+
+    Find camera translation that brings 3D joints S closest to 2D the corresponding joints_2d.
+    Input:
+        S: (B, 49, 3) 3D joint locations
+        joints: (B, 49, 3) 2D joint locations and confidence
+    Returns:
+        (B, 3) camera translation vectors
+    """
+
+    device = S.device
+    # Use only joints 25:49 (GT joints)
+    S = S[:, 25:, :].cpu().numpy()
+    joints_2d = joints_2d[:, 25:, :].cpu().numpy()
+    joints_conf = joints_2d[:, :, -1]
+    joints_2d = joints_2d[:, :, :-1]
+    trans = np.zeros((S.shape[0], 3), dtype=np.float32)
+    # Find the translation for each example in the batch
+    for i in range(S.shape[0]):
+        S_i = S[i]
+        joints_i = joints_2d[i]
+        conf_i = joints_conf[i]
+        trans[i] = estimate_translation_np(S_i, joints_i, conf_i, focal_length=focal_length, img_size=img_size)
+    return torch.from_numpy(trans).to(device)
+
+
+def rot6d_to_rotmat_spin(x):
+    """Convert 6D rotation representation to 3x3 rotation matrix.
+    Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019
+    Input:
+        (B,6) Batch of 6-D rotation representations
+    Output:
+        (B,3,3) Batch of corresponding rotation matrices
+    """
+    x = x.view(-1,3,2)
+    a1 = x[:, :, 0]
+    a2 = x[:, :, 1]
+    b1 = F.normalize(a1)
+    b2 = F.normalize(a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1)
+
+    # inp = a2 - torch.einsum('bi,bi->b', b1, a2).unsqueeze(-1) * b1
+    # denom = inp.pow(2).sum(dim=1).sqrt().unsqueeze(-1) + 1e-8
+    # b2 = inp / denom
+
+    b3 = torch.cross(b1, b2)
+    return torch.stack((b1, b2, b3), dim=-1)
+
+
+def rot6d_to_rotmat(x):
+    x = x.view(-1,3,2)
+
+    # Normalize the first vector
+    b1 = F.normalize(x[:, :, 0], dim=1, eps=1e-6)
+
+    dot_prod = torch.sum(b1 * x[:, :, 1], dim=1, keepdim=True)
+    # Compute the second vector by finding the orthogonal complement to it
+    b2 = F.normalize(x[:, :, 1] - dot_prod * b1, dim=-1, eps=1e-6)
+
+    # Finish building the basis by taking the cross product
+    b3 = torch.cross(b1, b2, dim=1)
+    rot_mats = torch.stack([b1, b2, b3], dim=-1)
+
+    return rot_mats
+
+
+def rigid_transform_3D(A, B):
+    n, dim = A.shape
+    centroid_A = np.mean(A, axis = 0)
+    centroid_B = np.mean(B, axis = 0)
+    H = np.dot(np.transpose(A - centroid_A), B - centroid_B) / n
+    U, s, V = np.linalg.svd(H)
+    R = np.dot(np.transpose(V), np.transpose(U))
+    if np.linalg.det(R) < 0:
+        s[-1] = -s[-1]
+        V[2] = -V[2]
+        R = np.dot(np.transpose(V), np.transpose(U))
+
+    varP = np.var(A, axis=0).sum()
+    c = 1/varP * np.sum(s)
+
+    t = -np.dot(c*R, np.transpose(centroid_A)) + np.transpose(centroid_B)
+    return c, R, t
+
+
+def rigid_align(A, B):
+    c, R, t = rigid_transform_3D(A, B)
+    A2 = np.transpose(np.dot(c*R, np.transpose(A))) + t
+    return A2
+
+def compute_error(output, target):
+    with torch.no_grad():
+        pred_verts = output[0]['verts'].reshape(-1, 6890, 3)
+        target_verts = target['verts'].reshape(-1, 6890, 3)
+
+        pred_j3ds = output[0]['kp_3d'].reshape(-1, 17, 3)
+        target_j3ds = target['kp_3d'].reshape(-1, 17, 3)
+
+        # mpve
+        pred_verts = pred_verts - pred_j3ds[:, :1, :]
+        target_verts = target_verts - target_j3ds[:, :1, :]
+        mpves = torch.sqrt(((pred_verts - target_verts) ** 2).sum(dim=-1)).mean(dim=-1).cpu()
+
+        # mpjpe
+        pred_j3ds = pred_j3ds - pred_j3ds[:, :1, :]
+        target_j3ds = target_j3ds - target_j3ds[:, :1, :]
+        mpjpes = torch.sqrt(((pred_j3ds - target_j3ds) ** 2).sum(dim=-1)).mean(dim=-1).cpu()
+        return mpjpes.mean(), mpves.mean()
+    
+def compute_error_frames(output, target):
+    with torch.no_grad():
+        pred_verts = output[0]['verts'].reshape(-1, 6890, 3)
+        target_verts = target['verts'].reshape(-1, 6890, 3)
+
+        pred_j3ds = output[0]['kp_3d'].reshape(-1, 17, 3)
+        target_j3ds = target['kp_3d'].reshape(-1, 17, 3)
+
+        # mpve
+        pred_verts = pred_verts - pred_j3ds[:, :1, :]
+        target_verts = target_verts - target_j3ds[:, :1, :]
+        mpves = torch.sqrt(((pred_verts - target_verts) ** 2).sum(dim=-1)).mean(dim=-1).cpu()
+
+        # mpjpe
+        pred_j3ds = pred_j3ds - pred_j3ds[:, :1, :]
+        target_j3ds = target_j3ds - target_j3ds[:, :1, :]
+        mpjpes = torch.sqrt(((pred_j3ds - target_j3ds) ** 2).sum(dim=-1)).mean(dim=-1).cpu()
+        return mpjpes, mpves
+
+def evaluate_mesh(results):
+    pred_verts = results['verts'].reshape(-1, 6890, 3)
+    target_verts = results['verts_gt'].reshape(-1, 6890, 3)
+
+    pred_j3ds = results['kp_3d'].reshape(-1, 17, 3)
+    target_j3ds = results['kp_3d_gt'].reshape(-1, 17, 3)
+    num_samples = pred_j3ds.shape[0]
+
+    # mpve
+    pred_verts = pred_verts - pred_j3ds[:, :1, :]
+    target_verts = target_verts - target_j3ds[:, :1, :]
+    mpve = np.mean(np.mean(np.sqrt(np.square(pred_verts - target_verts).sum(axis=2)), axis=1))
+
+
+    # mpjpe-17 & mpjpe-14
+    h36m_17_to_14 = (1, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 14, 15, 16)
+    pred_j3ds_17j = (pred_j3ds - pred_j3ds[:, :1, :])
+    target_j3ds_17j = (target_j3ds - target_j3ds[:, :1, :])
+
+    pred_j3ds = pred_j3ds_17j[:, h36m_17_to_14, :].copy()
+    target_j3ds = target_j3ds_17j[:, h36m_17_to_14, :].copy()
+
+    mpjpe = np.mean(np.sqrt(np.square(pred_j3ds - target_j3ds).sum(axis=2)), axis=1)  # (N, )
+    mpjpe_17j = np.mean(np.sqrt(np.square(pred_j3ds_17j - target_j3ds_17j).sum(axis=2)), axis=1)  # (N, )
+
+    pred_j3ds_pa, pred_j3ds_pa_17j = [], []
+    for n in range(num_samples):
+        pred_j3ds_pa.append(rigid_align(pred_j3ds[n], target_j3ds[n]))
+        pred_j3ds_pa_17j.append(rigid_align(pred_j3ds_17j[n], target_j3ds_17j[n]))
+    pred_j3ds_pa = np.array(pred_j3ds_pa)
+    pred_j3ds_pa_17j = np.array(pred_j3ds_pa_17j)
+
+    pa_mpjpe = np.mean(np.sqrt(np.square(pred_j3ds_pa - target_j3ds).sum(axis=2)), axis=1)  # (N, )
+    pa_mpjpe_17j = np.mean(np.sqrt(np.square(pred_j3ds_pa_17j - target_j3ds_17j).sum(axis=2)), axis=1)  # (N, )
+
+
+    error_dict = {
+        'mpve': mpve.mean(),
+        'mpjpe': mpjpe.mean(),
+        'pa_mpjpe': pa_mpjpe.mean(),
+        'mpjpe_17j': mpjpe_17j.mean(),
+        'pa_mpjpe_17j': pa_mpjpe_17j.mean(),
+        }
+    return error_dict
+
+
+def rectify_pose(pose):
+    """
+    Rectify "upside down" people in global coord
+
+    Args:
+        pose (72,): Pose.
+
+    Returns:
+        Rotated pose.
+    """
+    pose = pose.copy()
+    R_mod = cv2.Rodrigues(np.array([np.pi, 0, 0]))[0]
+    R_root = cv2.Rodrigues(pose[:3])[0]
+    new_root = R_root.dot(R_mod)
+    pose[:3] = cv2.Rodrigues(new_root)[0].reshape(3)
+    return pose
+
+def flip_thetas(thetas):
+        """Flip thetas.
+
+        Parameters
+        ----------
+        thetas : numpy.ndarray
+            Joints in shape (F, num_thetas, 3)
+        theta_pairs : list
+            List of theta pairs.
+
+        Returns
+        -------
+        numpy.ndarray
+            Flipped thetas with shape (F, num_thetas, 3)
+
+        """
+        #Joint pairs which defines the pairs of joint to be swapped when the image is flipped horizontally.
+        theta_pairs = ((1, 2), (4, 5), (7, 8), (10, 11), (13, 14), (16, 17), (18, 19), (20, 21), (22, 23))
+        thetas_flip = thetas.copy()
+        # reflect horizontally
+        thetas_flip[:, :, 1] = -1 * thetas_flip[:, :, 1]
+        thetas_flip[:, :, 2] = -1 * thetas_flip[:, :, 2]
+        # change left-right parts
+        for pair in theta_pairs:
+            thetas_flip[:, pair[0], :], thetas_flip[:, pair[1], :] = \
+                thetas_flip[:, pair[1], :], thetas_flip[:, pair[0], :].copy()
+        return thetas_flip
+    
+def flip_thetas_batch(thetas):
+    """Flip thetas in batch.
+
+    Parameters
+    ----------
+    thetas : numpy.array
+        Joints in shape (N, F, num_thetas*3)
+    theta_pairs : list
+        List of theta pairs.
+
+    Returns
+    -------
+    numpy.array
+        Flipped thetas with shape (N, F, num_thetas*3)
+
+    """
+    #Joint pairs which defines the pairs of joint to be swapped when the image is flipped horizontally.
+    theta_pairs = ((1, 2), (4, 5), (7, 8), (10, 11), (13, 14), (16, 17), (18, 19), (20, 21), (22, 23))
+    thetas_flip = copy.deepcopy(thetas).reshape(*thetas.shape[:2], 24, 3)
+    # reflect horizontally
+    thetas_flip[:, :, :, 1] = -1 * thetas_flip[:, :, :, 1]
+    thetas_flip[:, :, :, 2] = -1 * thetas_flip[:, :, :, 2]
+    # change left-right parts
+    for pair in theta_pairs:
+        thetas_flip[:, :, pair[0], :], thetas_flip[:, :, pair[1], :] = \
+            thetas_flip[:, :, pair[1], :], thetas_flip[:, :, pair[0], :].clone()
+
+    return thetas_flip.reshape(*thetas.shape[:2], -1)
+
+# def smpl_aa_to_ortho6d(smpl_aa):
+# #     [...,72] -> [...,144]
+#     rot_aa = smpl_aa.reshape([-1,24,3])
+#     rotmat = axis_angle_to_matrix(rot_aa)
+#     rot6d = matrix_to_rotation_6d(rotmat)
+#     rot6d = rot6d.reshape(-1,24*6)
+#     return rot6d

lib/utils/utils_smpl.py

@@ -0,0 +1,88 @@
+# This script is borrowed and extended from https://github.com/nkolot/SPIN/blob/master/models/hmr.py
+# Adhere to their licence to use this script
+
+import torch
+import numpy as np
+import os.path as osp
+from smplx import SMPL as _SMPL
+from smplx.utils import ModelOutput, SMPLOutput
+from smplx.lbs import vertices2joints
+
+
+# Map joints to SMPL joints
+JOINT_MAP = {
+    'OP Nose': 24, 'OP Neck': 12, 'OP RShoulder': 17,
+    'OP RElbow': 19, 'OP RWrist': 21, 'OP LShoulder': 16,
+    'OP LElbow': 18, 'OP LWrist': 20, 'OP MidHip': 0,
+    'OP RHip': 2, 'OP RKnee': 5, 'OP RAnkle': 8,
+    'OP LHip': 1, 'OP LKnee': 4, 'OP LAnkle': 7,
+    'OP REye': 25, 'OP LEye': 26, 'OP REar': 27,
+    'OP LEar': 28, 'OP LBigToe': 29, 'OP LSmallToe': 30,
+    'OP LHeel': 31, 'OP RBigToe': 32, 'OP RSmallToe': 33, 'OP RHeel': 34,
+    'Right Ankle': 8, 'Right Knee': 5, 'Right Hip': 45,
+    'Left Hip': 46, 'Left Knee': 4, 'Left Ankle': 7,
+    'Right Wrist': 21, 'Right Elbow': 19, 'Right Shoulder': 17,
+    'Left Shoulder': 16, 'Left Elbow': 18, 'Left Wrist': 20,
+    'Neck (LSP)': 47, 'Top of Head (LSP)': 48,
+    'Pelvis (MPII)': 49, 'Thorax (MPII)': 50,
+    'Spine (H36M)': 51, 'Jaw (H36M)': 52,
+    'Head (H36M)': 53, 'Nose': 24, 'Left Eye': 26,
+    'Right Eye': 25, 'Left Ear': 28, 'Right Ear': 27
+}
+JOINT_NAMES = [
+    'OP Nose', 'OP Neck', 'OP RShoulder',
+    'OP RElbow', 'OP RWrist', 'OP LShoulder',
+    'OP LElbow', 'OP LWrist', 'OP MidHip',
+    'OP RHip', 'OP RKnee', 'OP RAnkle',
+    'OP LHip', 'OP LKnee', 'OP LAnkle',
+    'OP REye', 'OP LEye', 'OP REar',
+    'OP LEar', 'OP LBigToe', 'OP LSmallToe',
+    'OP LHeel', 'OP RBigToe', 'OP RSmallToe', 'OP RHeel',
+    'Right Ankle', 'Right Knee', 'Right Hip',
+    'Left Hip', 'Left Knee', 'Left Ankle',
+    'Right Wrist', 'Right Elbow', 'Right Shoulder',
+    'Left Shoulder', 'Left Elbow', 'Left Wrist',
+    'Neck (LSP)', 'Top of Head (LSP)',
+    'Pelvis (MPII)', 'Thorax (MPII)',
+    'Spine (H36M)', 'Jaw (H36M)',
+    'Head (H36M)', 'Nose', 'Left Eye',
+    'Right Eye', 'Left Ear', 'Right Ear'
+]
+
+JOINT_IDS = {JOINT_NAMES[i]: i for i in range(len(JOINT_NAMES))}
+SMPL_MODEL_DIR = 'data/mesh'
+H36M_TO_J17 = [6, 5, 4, 1, 2, 3, 16, 15, 14, 11, 12, 13, 8, 10, 0, 7, 9]
+H36M_TO_J14 = H36M_TO_J17[:14]
+
+
+class SMPL(_SMPL):
+    """ Extension of the official SMPL implementation to support more joints """
+
+    def __init__(self, *args, **kwargs):
+        super(SMPL, self).__init__(*args, **kwargs)
+        joints = [JOINT_MAP[i] for i in JOINT_NAMES]
+        self.smpl_mean_params = osp.join(args[0], 'smpl_mean_params.npz')
+        J_regressor_extra = np.load(osp.join(args[0], 'J_regressor_extra.npy'))
+        self.register_buffer('J_regressor_extra', torch.tensor(J_regressor_extra, dtype=torch.float32))
+        J_regressor_h36m = np.load(osp.join(args[0], 'J_regressor_h36m_correct.npy'))
+        self.register_buffer('J_regressor_h36m', torch.tensor(J_regressor_h36m, dtype=torch.float32))
+        self.joint_map = torch.tensor(joints, dtype=torch.long)
+
+    def forward(self, *args, **kwargs):
+        kwargs['get_skin'] = True
+        smpl_output = super(SMPL, self).forward(*args, **kwargs)
+        extra_joints = vertices2joints(self.J_regressor_extra, smpl_output.vertices)
+        joints = torch.cat([smpl_output.joints, extra_joints], dim=1)
+        joints = joints[:, self.joint_map, :]
+        output = SMPLOutput(vertices=smpl_output.vertices,
+                            global_orient=smpl_output.global_orient,
+                            body_pose=smpl_output.body_pose,
+                            joints=joints,
+                            betas=smpl_output.betas,
+                            full_pose=smpl_output.full_pose)
+        return output
+
+
+def get_smpl_faces():
+    smpl = SMPL(SMPL_MODEL_DIR, batch_size=1, create_transl=False)
+    return smpl.faces

lib/utils/vismo.py

@@ -0,0 +1,345 @@
+import numpy as np
+import os
+import cv2
+import math
+import copy
+import imageio
+import io
+from tqdm import tqdm
+from PIL import Image
+from lib.utils.tools import ensure_dir
+import matplotlib
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from lib.utils.utils_smpl import *
+import ipdb
+
+def render_and_save(motion_input, save_path, keep_imgs=False, fps=25, color="#F96706#FB8D43#FDB381", with_conf=False, draw_face=False):
+    ensure_dir(os.path.dirname(save_path))
+    motion = copy.deepcopy(motion_input)
+    if motion.shape[-1]==2 or motion.shape[-1]==3:
+        motion = np.transpose(motion, (1,2,0))   #(T,17,D) -> (17,D,T) 
+    if motion.shape[1]==2 or with_conf:
+        colors = hex2rgb(color)
+        if not with_conf:
+            J, D, T = motion.shape
+            motion_full = np.ones([J,3,T])
+            motion_full[:,:2,:] = motion
+        else:
+            motion_full = motion
+        motion_full[:,:2,:] = pixel2world_vis_motion(motion_full[:,:2,:])
+        motion2video(motion_full, save_path=save_path, colors=colors, fps=fps)
+    elif motion.shape[0]==6890:
+        # motion_world = pixel2world_vis_motion(motion, dim=3)
+        motion2video_mesh(motion, save_path=save_path, keep_imgs=keep_imgs, fps=fps, draw_face=draw_face)
+    else:
+        motion_world = pixel2world_vis_motion(motion, dim=3)
+        motion2video_3d(motion_world, save_path=save_path, keep_imgs=keep_imgs, fps=fps)
+        
+def pixel2world_vis(pose):
+#     pose: (17,2)
+    return (pose + [1, 1]) * 512 / 2
+
+def pixel2world_vis_motion(motion, dim=2, is_tensor=False):
+#     pose: (17,2,N)
+    N = motion.shape[-1]
+    if dim==2:
+        offset = np.ones([2,N]).astype(np.float32)
+    else:
+        offset = np.ones([3,N]).astype(np.float32)
+        offset[2,:] = 0
+    if is_tensor:
+        offset = torch.tensor(offset)
+    return (motion + offset) * 512 / 2
+
+def vis_data_batch(data_input, data_label, n_render=10, save_path='doodle/vis_train_data/'):
+    '''
+        data_input: [N,T,17,2/3]
+        data_label: [N,T,17,3]
+    '''
+    pathlib.Path(save_path).mkdir(parents=True, exist_ok=True) 
+    for i in range(min(len(data_input), n_render)):
+        render_and_save(data_input[i][:,:,:2], '%s/input_%d.mp4' % (save_path, i))
+        render_and_save(data_label[i], '%s/gt_%d.mp4' % (save_path, i))
+
+def get_img_from_fig(fig, dpi=120):
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=dpi, bbox_inches="tight", pad_inches=0)
+    buf.seek(0)
+    img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
+    buf.close()
+    img = cv2.imdecode(img_arr, 1)
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGBA)
+    return img
+
+def rgb2rgba(color):
+    return (color[0], color[1], color[2], 255)
+
+def hex2rgb(hex, number_of_colors=3):
+    h = hex
+    rgb = []
+    for i in range(number_of_colors):
+        h = h.lstrip('#')
+        hex_color = h[0:6]
+        rgb_color = [int(hex_color[i:i+2], 16) for i in (0, 2 ,4)]
+        rgb.append(rgb_color)
+        h = h[6:]
+    return rgb
+
+def joints2image(joints_position, colors, transparency=False, H=1000, W=1000, nr_joints=49, imtype=np.uint8, grayscale=False, bg_color=(255, 255, 255)):
+#     joints_position: [17*2]
+    nr_joints = joints_position.shape[0]
+
+    if nr_joints == 49: # full joints(49): basic(15) + eyes(2) + toes(2) + hands(30)
+        limbSeq = [[0, 1], [1, 2], [1, 5], [1, 8], [2, 3], [3, 4], [5, 6], [6, 7], \
+                   [8, 9], [8, 13], [9, 10], [10, 11], [11, 12], [13, 14], [14, 15], [15, 16],
+                   ]#[0, 17], [0, 18]] #ignore eyes
+
+        L = rgb2rgba(colors[0]) if transparency else colors[0]
+        M = rgb2rgba(colors[1]) if transparency else colors[1]
+        R = rgb2rgba(colors[2]) if transparency else colors[2]
+
+        colors_joints = [M, M, L, L, L, R, R,
+                  R, M, L, L, L, L, R, R, R,
+                  R, R, L] + [L] * 15 + [R] * 15
+
+        colors_limbs = [M, L, R, M, L, L, R,
+                  R, L, R, L, L, L, R, R, R,
+                  R, R]
+    elif nr_joints == 15: # basic joints(15) + (eyes(2))
+        limbSeq = [[0, 1], [1, 2], [1, 5], [1, 8], [2, 3], [3, 4], [5, 6], [6, 7],
+                   [8, 9], [8, 12], [9, 10], [10, 11], [12, 13], [13, 14]]
+                    # [0, 15], [0, 16] two eyes are not drawn
+
+        L = rgb2rgba(colors[0]) if transparency else colors[0]
+        M = rgb2rgba(colors[1]) if transparency else colors[1]
+        R = rgb2rgba(colors[2]) if transparency else colors[2]
+
+        colors_joints = [M, M, L, L, L, R, R,
+                         R, M, L, L, L, R, R, R]
+
+        colors_limbs = [M, L, R, M, L, L, R,
+                        R, L, R, L, L, R, R]
+    elif nr_joints == 17: # H36M, 0: 'root',
+    #                             1: 'rhip',
+    #                             2: 'rkne',
+    #                             3: 'rank',
+    #                             4: 'lhip',
+    #                             5: 'lkne',
+    #                             6: 'lank',
+    #                             7: 'belly',
+    #                             8: 'neck',
+    #                             9: 'nose',
+    #                             10: 'head',
+    #                             11: 'lsho',
+    #                             12: 'lelb',
+    #                             13: 'lwri',
+    #                             14: 'rsho',
+    #                             15: 'relb',
+    #                             16: 'rwri'
+        limbSeq = [[0, 1], [1, 2], [2, 3], [0, 4], [4, 5], [5, 6], [0, 7], [7, 8], [8, 9], [8, 11], [8, 14], [9, 10], [11, 12], [12, 13], [14, 15], [15, 16]]
+
+        L = rgb2rgba(colors[0]) if transparency else colors[0]
+        M = rgb2rgba(colors[1]) if transparency else colors[1]
+        R = rgb2rgba(colors[2]) if transparency else colors[2]
+
+        colors_joints = [M, R, R, R, L, L, L, M, M, M, M, L, L, L, R, R, R]
+        colors_limbs = [R, R, R, L, L, L, M, M, M, L, R, M, L, L, R, R]
+        
+    else:
+        raise ValueError("Only support number of joints be 49 or 17 or 15")
+
+    if transparency:
+        canvas = np.zeros(shape=(H, W, 4))
+    else:
+        canvas = np.ones(shape=(H, W, 3)) * np.array(bg_color).reshape([1, 1, 3])
+    hips = joints_position[0]
+    neck = joints_position[8]
+    torso_length = ((hips[1] - neck[1]) ** 2 + (hips[0] - neck[0]) ** 2) ** 0.5
+    head_radius = int(torso_length/4.5)
+    end_effectors_radius = int(torso_length/15)
+    end_effectors_radius = 7
+    joints_radius = 7
+    for i in range(0, len(colors_joints)):
+        if i in (17, 18):
+            continue
+        elif i > 18:
+            radius = 2
+        else:
+            radius = joints_radius
+        if len(joints_position[i])==3:                 # If there is confidence, weigh by confidence
+            weight = joints_position[i][2]
+            if weight==0:
+                continue
+        cv2.circle(canvas, (int(joints_position[i][0]),int(joints_position[i][1])), radius, colors_joints[i], thickness=-1)
+        
+    stickwidth = 2
+    for i in range(len(limbSeq)):
+        limb = limbSeq[i]
+        cur_canvas = canvas.copy()
+        point1_index = limb[0]
+        point2_index = limb[1]
+        point1 = joints_position[point1_index]
+        point2 = joints_position[point2_index]
+        if len(point1)==3:                             # If there is confidence, weigh by confidence
+            limb_weight = min(point1[2], point2[2])
+            if limb_weight==0:
+                bb = bounding_box(canvas)
+                canvas_cropped = canvas[:,bb[2]:bb[3], :]
+                continue
+        X = [point1[1], point2[1]]
+        Y = [point1[0], point2[0]]
+        mX = np.mean(X)
+        mY = np.mean(Y)
+        length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
+        alpha = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
+        polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(alpha), 0, 360, 1)
+        cv2.fillConvexPoly(cur_canvas, polygon, colors_limbs[i])
+        canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
+        bb = bounding_box(canvas)
+        canvas_cropped = canvas[:,bb[2]:bb[3], :]
+    canvas = canvas.astype(imtype)
+    canvas_cropped = canvas_cropped.astype(imtype)
+    if grayscale:
+        if transparency:
+            canvas = cv2.cvtColor(canvas, cv2.COLOR_RGBA2GRAY)
+            canvas_cropped = cv2.cvtColor(canvas_cropped, cv2.COLOR_RGBA2GRAY)
+        else:
+            canvas = cv2.cvtColor(canvas, cv2.COLOR_RGB2GRAY)
+            canvas_cropped = cv2.cvtColor(canvas_cropped, cv2.COLOR_RGB2GRAY)
+    return [canvas, canvas_cropped]
+
+
+def motion2video(motion, save_path, colors, h=512, w=512, bg_color=(255, 255, 255), transparency=False, motion_tgt=None, fps=25, save_frame=False, grayscale=False, show_progress=True, as_array=False):
+    nr_joints = motion.shape[0]
+#     as_array = save_path.endswith(".npy")
+    vlen = motion.shape[-1]
+
+    out_array = np.zeros([vlen, h, w, 3]) if as_array else None
+    videowriter = None if as_array else imageio.get_writer(save_path, fps=fps)
+
+    if save_frame:
+        frames_dir = save_path[:-4] + '-frames'
+        ensure_dir(frames_dir)
+
+    iterator = range(vlen)
+    if show_progress: iterator = tqdm(iterator)
+    for i in iterator:
+        [img, img_cropped] = joints2image(motion[:, :, i], colors, transparency=transparency, bg_color=bg_color, H=h, W=w, nr_joints=nr_joints, grayscale=grayscale)
+        if motion_tgt is not None:
+            [img_tgt, img_tgt_cropped] = joints2image(motion_tgt[:, :, i], colors, transparency=transparency, bg_color=bg_color, H=h, W=w, nr_joints=nr_joints, grayscale=grayscale)
+            img_ori = img.copy()
+            img = cv2.addWeighted(img_tgt, 0.3, img_ori, 0.7, 0)
+            img_cropped = cv2.addWeighted(img_tgt, 0.3, img_ori, 0.7, 0)
+            bb = bounding_box(img_cropped)
+            img_cropped = img_cropped[:, bb[2]:bb[3], :]
+        if save_frame:
+            save_image(img_cropped, os.path.join(frames_dir, "%04d.png" % i))
+        if as_array: out_array[i] = img
+        else: videowriter.append_data(img)
+
+    if not as_array:
+        videowriter.close()
+
+    return out_array
+
+def motion2video_3d(motion, save_path, fps=25, keep_imgs = False):
+#     motion: (17,3,N)
+    videowriter = imageio.get_writer(save_path, fps=fps)
+    vlen = motion.shape[-1]
+    save_name = save_path.split('.')[0]
+    frames = []
+    joint_pairs = [[0, 1], [1, 2], [2, 3], [0, 4], [4, 5], [5, 6], [0, 7], [7, 8], [8, 9], [8, 11], [8, 14], [9, 10], [11, 12], [12, 13], [14, 15], [15, 16]]
+    joint_pairs_left = [[8, 11], [11, 12], [12, 13], [0, 4], [4, 5], [5, 6]]
+    joint_pairs_right = [[8, 14], [14, 15], [15, 16], [0, 1], [1, 2], [2, 3]]
+    
+    color_mid = "#00457E"
+    color_left = "#02315E"
+    color_right = "#2F70AF"
+    for f in tqdm(range(vlen)):
+        j3d = motion[:,:,f]
+        fig = plt.figure(0, figsize=(10, 10))
+        ax = plt.axes(projection="3d")
+        ax.set_xlim(-512, 0)
+        ax.set_ylim(-256, 256)
+        ax.set_zlim(-512, 0)
+        # ax.set_xlabel('X')
+        # ax.set_ylabel('Y')
+        # ax.set_zlabel('Z')
+        ax.view_init(elev=12., azim=80)
+        plt.tick_params(left = False, right = False , labelleft = False ,
+                        labelbottom = False, bottom = False)
+        for i in range(len(joint_pairs)):
+            limb = joint_pairs[i]
+            xs, ys, zs = [np.array([j3d[limb[0], j], j3d[limb[1], j]]) for j in range(3)]
+            if joint_pairs[i] in joint_pairs_left:
+                ax.plot(-xs, -zs, -ys, color=color_left, lw=3, marker='o', markerfacecolor='w', markersize=3, markeredgewidth=2) # axis transformation for visualization
+            elif joint_pairs[i] in joint_pairs_right:
+                ax.plot(-xs, -zs, -ys, color=color_right, lw=3, marker='o', markerfacecolor='w', markersize=3, markeredgewidth=2) # axis transformation for visualization
+            else:
+                ax.plot(-xs, -zs, -ys, color=color_mid, lw=3, marker='o', markerfacecolor='w', markersize=3, markeredgewidth=2) # axis transformation for visualization
+            
+        frame_vis = get_img_from_fig(fig)
+        videowriter.append_data(frame_vis)
+    videowriter.close()
+
+def motion2video_mesh(motion, save_path, fps=25, keep_imgs = False, draw_face=True):
+    videowriter = imageio.get_writer(save_path, fps=fps)
+    vlen = motion.shape[-1]
+    draw_skele = (motion.shape[0]==17)
+    save_name = save_path.split('.')[0]
+    smpl_faces = get_smpl_faces()
+    frames = []
+    joint_pairs = [[0, 1], [1, 2], [2, 3], [0, 4], [4, 5], [5, 6], [0, 7], [7, 8], [8, 9], [8, 11], [8, 14], [9, 10], [11, 12], [12, 13], [14, 15], [15, 16]]
+
+    
+    X, Y, Z = motion[:, 0], motion[:, 1], motion[:, 2]
+    max_range = np.array([X.max()-X.min(), Y.max()-Y.min(), Z.max()-Z.min()]).max() / 2.0
+    mid_x = (X.max()+X.min()) * 0.5
+    mid_y = (Y.max()+Y.min()) * 0.5
+    mid_z = (Z.max()+Z.min()) * 0.5
+    
+    for f in tqdm(range(vlen)):
+        j3d = motion[:,:,f]
+        plt.gca().set_axis_off()
+        plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
+        plt.gca().xaxis.set_major_locator(plt.NullLocator())
+        plt.gca().yaxis.set_major_locator(plt.NullLocator())
+        fig = plt.figure(0, figsize=(8, 8))
+        ax = plt.axes(projection="3d", proj_type = 'ortho')
+        ax.set_xlim(mid_x - max_range, mid_x + max_range)
+        ax.set_ylim(mid_y - max_range, mid_y + max_range)
+        ax.set_zlim(mid_z - max_range, mid_z + max_range)
+        ax.view_init(elev=-90, azim=-90)
+        plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
+        plt.margins(0, 0, 0)
+        plt.gca().xaxis.set_major_locator(plt.NullLocator())
+        plt.gca().yaxis.set_major_locator(plt.NullLocator())
+        plt.axis('off')
+        plt.xticks([])
+        plt.yticks([])
+        
+        # plt.savefig("filename.png", transparent=True, bbox_inches="tight", pad_inches=0)
+        
+        if draw_skele:
+            for i in range(len(joint_pairs)):
+                limb = joint_pairs[i]
+                xs, ys, zs = [np.array([j3d[limb[0], j], j3d[limb[1], j]]) for j in range(3)]
+                ax.plot(-xs, -zs, -ys, c=[0,0,0], lw=3, marker='o', markerfacecolor='w', markersize=3, markeredgewidth=2) # axis transformation for visualization
+        elif draw_face:
+            ax.plot_trisurf(j3d[:, 0], j3d[:, 1], triangles=smpl_faces, Z=j3d[:, 2], color=(166/255.0,188/255.0,218/255.0,0.9))
+        else:
+            ax.scatter(j3d[:, 0], j3d[:, 1], j3d[:, 2], s=3, c='w', edgecolors='grey')
+        frame_vis = get_img_from_fig(fig, dpi=128)
+        plt.cla()
+        videowriter.append_data(frame_vis)
+    videowriter.close()
+
+def save_image(image_numpy, image_path):
+    image_pil = Image.fromarray(image_numpy)
+    image_pil.save(image_path)
+
+def bounding_box(img):
+    a = np.where(img != 0)
+    bbox = np.min(a[0]), np.max(a[0]), np.min(a[1]), np.max(a[1])
+    return bbox

params/d2c_params.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3b02023c3fc660f4808c735e2f8a9eae1206a411f1ad7e3429d33719da1cd0d1
+size 184

params/synthetic_noise.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c801dfb859b08cf2ed96012176b0dcc7af2358d1a5d18a7c72b6e944416297b
+size 1997

requirements.txt

@@ -0,0 +1,12 @@
+tensorboardX
+tqdm
+easydict
+prettytable
+chumpy
+opencv-python
+imageio-ffmpeg
+matplotlib==3.1.1
+roma
+ipdb
+pytorch-metric-learning                         # For one-hot action recognition
+smplx[all]                                      # For mesh recovery

tools/compress_amass.py

@@ -0,0 +1,62 @@
+import numpy as np
+import os
+import pickle
+
+raw_dir = './data/AMASS/amass_202203/'
+processed_dir = './data/AMASS/amass_fps60'
+os.makedirs(processed_dir, exist_ok=True)
+
+files = []
+length = 0
+target_fps = 60
+
+def traverse(f):
+    fs = os.listdir(f)
+    for f1 in fs:
+        tmp_path = os.path.join(f,f1)
+        # file
+        if not os.path.isdir(tmp_path):
+            files.append(tmp_path)
+        # dir
+        else:
+            traverse(tmp_path)
+
+traverse(raw_dir)
+
+print('files:', len(files))
+
+fnames = []
+all_motions = []
+
+with open('data/AMASS/fps.csv', 'w') as f:
+    print('fname_new, len_ori, fps, len_new', file=f)
+    for fname in sorted(files):
+        try:
+            raw_x = np.load(fname)
+            x = dict(raw_x)
+            fps = x['mocap_framerate']
+            len_ori = len(x['trans'])
+            sample_stride = round(fps / target_fps)
+            x['mocap_framerate'] = target_fps
+            x['trans'] = x['trans'][::sample_stride]
+            x['dmpls'] = x['dmpls'][::sample_stride]
+            x['poses'] = x['poses'][::sample_stride]
+            fname_new = '_'.join(fname.split('/')[2:])
+            len_new = len(x['trans'])
+            
+            length += len_new
+            print(fname_new, ',', len_ori, ',', fps, ',', len_new, file=f)
+            fnames.append(fname_new)
+            all_motions.append(x)
+            np.savez('%s/%s' % (processed_dir, fname_new), x)
+        except:
+            pass
+        
+#         break
+
+print('poseFrame:', length)
+print('motions:', len(fnames))
+
+with open("data/AMASS/all_motions_fps%d.pkl" % target_fps, "wb") as myprofile:  
+    pickle.dump(all_motions, myprofile)
+