Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,522 Bytes
352b049 |
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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 |
# This code is based on https://github.com/Mathux/ACTOR.git
import contextlib
import numpy as np
import torch
from smplx import SMPLLayer as _SMPLLayer
from smplx.lbs import vertices2joints
# action2motion_joints = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 21, 24, 38]
# change 0 and 8
action2motion_joints = [8, 1, 2, 3, 4, 5, 6, 7, 0, 9, 10, 11, 12, 13, 14, 21, 24, 38]
# from utils.config import JOINT_REGRESSOR_TRAIN_EXTRA, SMPL_MODEL_PATH
import os
SMPL_DATA_PATH = "./body_models/smpl"
# SMPL_KINTREE_PATH = os.path.join(SMPL_DATA_PATH, "kintree_table.pkl")
SMPL_MODEL_PATH = os.path.join(SMPL_DATA_PATH, "SMPL_NEUTRAL.pkl")
JOINT_REGRESSOR_TRAIN_EXTRA = os.path.join(SMPL_DATA_PATH, "J_regressor_extra.npy")
# ROT_CONVENTION_TO_ROT_NUMBER = {
# 'legacy': 23,
# 'no_hands': 21,
# 'full_hands': 51,
# 'mitten_hands': 33,
# }
# GENDERS = ['neutral', 'male', 'female']
# NUM_BETAS = 10
JOINTSTYPE_ROOT = {
"a2m": 0, # action2motion
"smpl": 0,
"a2mpl": 0, # set(smpl, a2m)
"vibe": 8,
} # 0 is the 8 position: OP MidHip below
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",
]
# adapted from VIBE/SPIN to output smpl_joints, vibe joints and action2motion joints
class SMPL(_SMPLLayer):
"""Extension of the official SMPL implementation to support more joints"""
def __init__(self, model_path=SMPL_MODEL_PATH, **kwargs):
kwargs["model_path"] = model_path
# remove the verbosity for the 10-shapes beta parameters
with contextlib.redirect_stdout(None):
super(SMPL, self).__init__(**kwargs)
J_regressor_extra = np.load(JOINT_REGRESSOR_TRAIN_EXTRA)
self.register_buffer(
"J_regressor_extra", torch.tensor(J_regressor_extra, dtype=torch.float32)
)
vibe_indexes = np.array([JOINT_MAP[i] for i in JOINT_NAMES])
a2m_indexes = vibe_indexes[action2motion_joints]
smpl_indexes = np.arange(24)
a2mpl_indexes = np.unique(np.r_[smpl_indexes, a2m_indexes])
self.maps = {
"vibe": vibe_indexes,
"a2m": a2m_indexes,
"smpl": smpl_indexes,
"a2mpl": a2mpl_indexes,
}
def forward(self, *args, **kwargs):
smpl_output = super(SMPL, self).forward(*args, **kwargs)
extra_joints = vertices2joints(self.J_regressor_extra, smpl_output.vertices)
all_joints = torch.cat([smpl_output.joints, extra_joints], dim=1)
output = {"vertices": smpl_output.vertices}
for joinstype, indexes in self.maps.items():
output[joinstype] = all_joints[:, indexes]
return output
|