metadata

thumbnail: https://motionbert.github.io/assets/teaser.gif
tags:
  - 3D Human Pose Estimation
  - Skeleton-based Action Recognition
  - Mesh Recovery
arxiv: '2210.06551'

MotionBERT

This is the official PyTorch implementation of the paper "Learning Human Motion Representations: A Unified Perspective".

Installation

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
3D human pose estimation	docs/pose3d.md
Skeleton-based action recognition	docs/action.md
Mesh recovery	docs/mesh.md

Applications

In-the-wild inference (for custom videos)

Please refer to docs/inference.md.

Using MotionBERT for human-centric video representations

'''	    
  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

The model could handle different input lengths (no more than 243 frames). No need to explicitly specify the input length elsewhere.

The model uses 17 body keypoints (H36M format). If you are using other formats, please convert them before feeding to MotionBERT.

Please refer to model_action.py and model_mesh.py for examples of (easily) adapting MotionBERT to different downstream tasks.

For RGB videos, you need to extract 2D poses (inference.md), convert the keypoint format (dataset_wild.py), and then feed to MotionBERT (infer_wild.py).

Model Zoo

Model	Download Link	Config	Performance
MotionBERT (162MB)	HuggingFace	pretrain/MB_pretrain.yaml	-
MotionBERT-Lite (61MB)	HuggingFace	pretrain/MB_lite.yaml	-
3D Pose (H36M-SH, scratch)	HuggingFace	pose3d/MB_train_h36m.yaml	39.2mm (MPJPE)
3D Pose (H36M-SH, ft)	HuggingFace	pose3d/MB_ft_h36m.yaml	37.2mm (MPJPE)
Action Recognition (x-sub, ft)	HuggingFace	action/MB_ft_NTU60_xsub.yaml	97.2% (Top1 Acc)
Action Recognition (x-view, ft)	HuggingFace	action/MB_ft_NTU60_xview.yaml	93.0% (Top1 Acc)
Mesh (with 3DPW, ft)	HuggingFace	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

Scripts and docs for pretraining
Demo for custom videos

Citation

If you find our work useful for your project, please consider citing the paper:

@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},
}