Spaces:
Runtime error
Runtime error
File size: 3,442 Bytes
2d5f249 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
# 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
|