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# 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 |