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# This code is based on https://github.com/Mathux/ACTOR.git
import numpy as np
import torch
import contextlib
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 SMPL_MODEL_PATH, JOINT_REGRESSOR_TRAIN_EXTRA
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 |