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import torch | |
import numpy as np | |
import pickle | |
from typing import Optional | |
import smplx | |
from smplx.lbs import vertices2joints | |
from smplx.utils import SMPLOutput | |
class SMPL(smplx.SMPLLayer): | |
def __init__(self, *args, joint_regressor_extra: Optional[str] = None, update_hips: bool = False, **kwargs): | |
""" | |
Extension of the official SMPL implementation to support more joints. | |
Args: | |
Same as SMPLLayer. | |
joint_regressor_extra (str): Path to extra joint regressor. | |
""" | |
super(SMPL, self).__init__(*args, **kwargs) | |
smpl_to_openpose = [24, 12, 17, 19, 21, 16, 18, 20, 0, 2, 5, 8, 1, 4, | |
7, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34] | |
if joint_regressor_extra is not None: | |
self.register_buffer('joint_regressor_extra', torch.tensor(pickle.load(open(joint_regressor_extra, 'rb'), encoding='latin1'), dtype=torch.float32)) | |
self.register_buffer('joint_map', torch.tensor(smpl_to_openpose, dtype=torch.long)) | |
self.update_hips = update_hips | |
def forward(self, *args, **kwargs) -> SMPLOutput: | |
""" | |
Run forward pass. Same as SMPL and also append an extra set of joints if joint_regressor_extra is specified. | |
""" | |
smpl_output = super(SMPL, self).forward(*args, **kwargs) | |
joints = smpl_output.joints[:, self.joint_map, :] | |
if self.update_hips: | |
joints[:,[9,12]] = joints[:,[9,12]] + \ | |
0.25*(joints[:,[9,12]]-joints[:,[12,9]]) + \ | |
0.5*(joints[:,[8]] - 0.5*(joints[:,[9,12]] + joints[:,[12,9]])) | |
if hasattr(self, 'joint_regressor_extra'): | |
extra_joints = vertices2joints(self.joint_regressor_extra, smpl_output.vertices) | |
joints = torch.cat([joints, extra_joints], dim=1) | |
smpl_output.joints = joints | |
return smpl_output | |