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| # This script is borrowed from https://github.com/nkolot/SPIN/blob/master/models/smpl.py | |
| import torch | |
| import numpy as np | |
| from lib.smplx import SMPL as _SMPL | |
| from lib.smplx.body_models import ModelOutput | |
| from lib.smplx.lbs import vertices2joints | |
| from collections import namedtuple | |
| from lib.pymaf.core import path_config, constants | |
| SMPL_MEAN_PARAMS = path_config.SMPL_MEAN_PARAMS | |
| SMPL_MODEL_DIR = path_config.SMPL_MODEL_DIR | |
| # Indices to get the 14 LSP joints from the 17 H36M joints | |
| 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().__init__(*args, **kwargs) | |
| joints = [constants.JOINT_MAP[i] for i in constants.JOINT_NAMES] | |
| J_regressor_extra = np.load(path_config.JOINT_REGRESSOR_TRAIN_EXTRA) | |
| self.register_buffer( | |
| 'J_regressor_extra', | |
| torch.tensor(J_regressor_extra, dtype=torch.float32)) | |
| self.joint_map = torch.tensor(joints, dtype=torch.long) | |
| self.ModelOutput = namedtuple( | |
| 'ModelOutput_', ModelOutput._fields + ( | |
| 'smpl_joints', | |
| 'joints_J19', | |
| )) | |
| self.ModelOutput.__new__.__defaults__ = (None, ) * len( | |
| self.ModelOutput._fields) | |
| def forward(self, *args, **kwargs): | |
| kwargs['get_skin'] = True | |
| smpl_output = super().forward(*args, **kwargs) | |
| extra_joints = vertices2joints(self.J_regressor_extra, | |
| smpl_output.vertices) | |
| # smpl_output.joints: [B, 45, 3] extra_joints: [B, 9, 3] | |
| vertices = smpl_output.vertices | |
| joints = torch.cat([smpl_output.joints, extra_joints], dim=1) | |
| smpl_joints = smpl_output.joints[:, :24] | |
| joints = joints[:, self.joint_map, :] # [B, 49, 3] | |
| joints_J24 = joints[:, -24:, :] | |
| joints_J19 = joints_J24[:, constants.J24_TO_J19, :] | |
| output = self.ModelOutput(vertices=vertices, | |
| global_orient=smpl_output.global_orient, | |
| body_pose=smpl_output.body_pose, | |
| joints=joints, | |
| joints_J19=joints_J19, | |
| smpl_joints=smpl_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 | |
| def get_part_joints(smpl_joints): | |
| batch_size = smpl_joints.shape[0] | |
| # part_joints = torch.zeros().to(smpl_joints.device) | |
| one_seg_pairs = [(0, 1), (0, 2), (0, 3), (3, 6), (9, 12), (9, 13), (9, 14), | |
| (12, 15), (13, 16), (14, 17)] | |
| two_seg_pairs = [(1, 4), (2, 5), (4, 7), (5, 8), (16, 18), (17, 19), | |
| (18, 20), (19, 21)] | |
| one_seg_pairs.extend(two_seg_pairs) | |
| single_joints = [(10), (11), (15), (22), (23)] | |
| part_joints = [] | |
| for j_p in one_seg_pairs: | |
| new_joint = torch.mean(smpl_joints[:, j_p], dim=1, keepdim=True) | |
| part_joints.append(new_joint) | |
| for j_p in single_joints: | |
| part_joints.append(smpl_joints[:, j_p:j_p + 1]) | |
| part_joints = torch.cat(part_joints, dim=1) | |
| return part_joints | |