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import torch
import torch.nn as nn
import numpy as np
from lib.pymafx.core import constants
from lib.common.config import cfg
from lib.pymafx.utils.geometry import rot6d_to_rotmat, rotmat_to_rot6d, projection, rotation_matrix_to_angle_axis, compute_twist_rotation
from .maf_extractor import MAF_Extractor, Mesh_Sampler
from .smpl import SMPL, SMPL_MODEL_DIR, SMPL_MEAN_PARAMS, get_partial_smpl, SMPL_Family
from lib.smplx.lbs import batch_rodrigues
from .res_module import IUV_predict_layer
from .hr_module import get_hrnet_encoder
from .pose_resnet import get_resnet_encoder
from lib.pymafx.utils.imutils import j2d_processing
from lib.pymafx.utils.cam_params import homo_vector
from .attention import get_att_block
import logging
logger = logging.getLogger(__name__)
BN_MOMENTUM = 0.1
class Regressor(nn.Module):
def __init__(
self,
feat_dim,
smpl_mean_params,
use_cam_feats=False,
feat_dim_hand=0,
feat_dim_face=0,
bhf_names=['body'],
smpl_models={}
):
super().__init__()
npose = 24 * 6
shape_dim = 10
cam_dim = 3
hand_dim = 15 * 6
face_dim = 3 * 6 + 10
self.body_feat_dim = feat_dim
self.smpl_mode = (cfg.MODEL.MESH_MODEL == 'smpl')
self.smplx_mode = (cfg.MODEL.MESH_MODEL == 'smplx')
self.use_cam_feats = use_cam_feats
cam_feat_len = 4 if self.use_cam_feats else 0
self.bhf_names = bhf_names
self.hand_only_mode = (cfg.TRAIN.BHF_MODE == 'hand_only')
self.face_only_mode = (cfg.TRAIN.BHF_MODE == 'face_only')
self.body_hand_mode = (cfg.TRAIN.BHF_MODE == 'body_hand')
self.full_body_mode = (cfg.TRAIN.BHF_MODE == 'full_body')
# if self.use_cam_feats:
# assert cfg.MODEL.USE_IWP_CAM is False
if 'body' in self.bhf_names:
self.fc1 = nn.Linear(feat_dim + npose + cam_feat_len + shape_dim + cam_dim, 1024)
self.drop1 = nn.Dropout()
self.fc2 = nn.Linear(1024, 1024)
self.drop2 = nn.Dropout()
self.decpose = nn.Linear(1024, npose)
self.decshape = nn.Linear(1024, 10)
self.deccam = nn.Linear(1024, 3)
nn.init.xavier_uniform_(self.decpose.weight, gain=0.01)
nn.init.xavier_uniform_(self.decshape.weight, gain=0.01)
nn.init.xavier_uniform_(self.deccam.weight, gain=0.01)
if not self.smpl_mode:
if self.hand_only_mode:
self.part_names = ['rhand']
elif self.face_only_mode:
self.part_names = ['face']
elif self.body_hand_mode:
self.part_names = ['lhand', 'rhand']
elif self.full_body_mode:
self.part_names = ['lhand', 'rhand', 'face']
else:
self.part_names = []
if 'rhand' in self.part_names:
# self.fc1_hand = nn.Linear(feat_dim_hand + hand_dim + rh_orient_dim + rh_shape_dim + rh_cam_dim, 1024)
self.fc1_hand = nn.Linear(feat_dim_hand + hand_dim, 1024)
self.drop1_hand = nn.Dropout()
self.fc2_hand = nn.Linear(1024, 1024)
self.drop2_hand = nn.Dropout()
# self.declhand = nn.Linear(1024, 15*6)
self.decrhand = nn.Linear(1024, 15 * 6)
# nn.init.xavier_uniform_(self.declhand.weight, gain=0.01)
nn.init.xavier_uniform_(self.decrhand.weight, gain=0.01)
if cfg.MODEL.MESH_MODEL == 'mano' or cfg.MODEL.PyMAF.OPT_WRIST:
rh_cam_dim = 3
rh_orient_dim = 6
rh_shape_dim = 10
self.fc3_hand = nn.Linear(
1024 + rh_orient_dim + rh_shape_dim + rh_cam_dim, 1024
)
self.drop3_hand = nn.Dropout()
self.decshape_rhand = nn.Linear(1024, 10)
self.decorient_rhand = nn.Linear(1024, 6)
self.deccam_rhand = nn.Linear(1024, 3)
nn.init.xavier_uniform_(self.decshape_rhand.weight, gain=0.01)
nn.init.xavier_uniform_(self.decorient_rhand.weight, gain=0.01)
nn.init.xavier_uniform_(self.deccam_rhand.weight, gain=0.01)
if 'face' in self.part_names:
self.fc1_face = nn.Linear(feat_dim_face + face_dim, 1024)
self.drop1_face = nn.Dropout()
self.fc2_face = nn.Linear(1024, 1024)
self.drop2_face = nn.Dropout()
self.dechead = nn.Linear(1024, 3 * 6)
self.decexp = nn.Linear(1024, 10)
nn.init.xavier_uniform_(self.dechead.weight, gain=0.01)
nn.init.xavier_uniform_(self.decexp.weight, gain=0.01)
if cfg.MODEL.MESH_MODEL == 'flame':
rh_cam_dim = 3
rh_orient_dim = 6
rh_shape_dim = 10
self.fc3_face = nn.Linear(
1024 + rh_orient_dim + rh_shape_dim + rh_cam_dim, 1024
)
self.drop3_face = nn.Dropout()
self.decshape_face = nn.Linear(1024, 10)
self.decorient_face = nn.Linear(1024, 6)
self.deccam_face = nn.Linear(1024, 3)
nn.init.xavier_uniform_(self.decshape_face.weight, gain=0.01)
nn.init.xavier_uniform_(self.decorient_face.weight, gain=0.01)
nn.init.xavier_uniform_(self.deccam_face.weight, gain=0.01)
if self.smplx_mode and cfg.MODEL.PyMAF.PRED_VIS_H:
self.fc1_vis = nn.Linear(1024 + 1024 + 1024, 1024)
self.drop1_vis = nn.Dropout()
self.fc2_vis = nn.Linear(1024, 1024)
self.drop2_vis = nn.Dropout()
self.decvis = nn.Linear(1024, 2)
nn.init.xavier_uniform_(self.decvis.weight, gain=0.01)
if 'body' in smpl_models:
self.smpl = smpl_models['body']
if 'hand' in smpl_models:
self.mano = smpl_models['hand']
if 'face' in smpl_models:
self.flame = smpl_models['face']
if cfg.MODEL.PyMAF.OPT_WRIST:
self.body_model = SMPL(model_path=SMPL_MODEL_DIR, batch_size=64, create_transl=False)
mean_params = np.load(smpl_mean_params)
init_pose = torch.from_numpy(mean_params['pose'][:]).unsqueeze(0)
init_shape = torch.from_numpy(mean_params['shape'][:].astype('float32')).unsqueeze(0)
init_cam = torch.from_numpy(mean_params['cam']).unsqueeze(0)
self.register_buffer('init_pose', init_pose)
self.register_buffer('init_shape', init_shape)
self.register_buffer('init_cam', init_cam)
self.register_buffer('init_orient', init_pose[:, :6])
self.flip_vector = torch.ones((1, 9), dtype=torch.float32)
self.flip_vector[:, [1, 2, 3, 6]] *= -1
self.flip_vector = self.flip_vector.reshape(1, 3, 3)
if not self.smpl_mode:
lhand_mean_rot6d = rotmat_to_rot6d(
batch_rodrigues(self.smpl.model.model_neutral.left_hand_mean.view(-1, 3)).view(
[-1, 3, 3]
)
)
rhand_mean_rot6d = rotmat_to_rot6d(
batch_rodrigues(self.smpl.model.model_neutral.right_hand_mean.view(-1, 3)).view(
[-1, 3, 3]
)
)
init_lhand = lhand_mean_rot6d.reshape(-1).unsqueeze(0)
init_rhand = rhand_mean_rot6d.reshape(-1).unsqueeze(0)
# init_hand = torch.cat([init_lhand, init_rhand]).unsqueeze(0)
init_face = rotmat_to_rot6d(torch.stack([torch.eye(3)] * 3)).reshape(-1).unsqueeze(0)
init_exp = torch.zeros(10).unsqueeze(0)
if self.smplx_mode or 'hand' in bhf_names:
# init_hand = torch.cat([init_lhand, init_rhand]).unsqueeze(0)
self.register_buffer('init_lhand', init_lhand)
self.register_buffer('init_rhand', init_rhand)
if self.smplx_mode or 'face' in bhf_names:
self.register_buffer('init_face', init_face)
self.register_buffer('init_exp', init_exp)
def forward(
self,
x=None,
n_iter=1,
J_regressor=None,
rw_cam={},
init_mode=False,
global_iter=-1,
**kwargs
):
if x is not None:
batch_size = x.shape[0]
else:
if 'xc_rhand' in kwargs:
batch_size = kwargs['xc_rhand'].shape[0]
elif 'xc_face' in kwargs:
batch_size = kwargs['xc_face'].shape[0]
if 'body' in self.bhf_names:
if 'init_pose' not in kwargs:
kwargs['init_pose'] = self.init_pose.expand(batch_size, -1)
if 'init_shape' not in kwargs:
kwargs['init_shape'] = self.init_shape.expand(batch_size, -1)
if 'init_cam' not in kwargs:
kwargs['init_cam'] = self.init_cam.expand(batch_size, -1)
pred_cam = kwargs['init_cam']
pred_pose = kwargs['init_pose']
pred_shape = kwargs['init_shape']
if self.full_body_mode or self.body_hand_mode:
if cfg.MODEL.PyMAF.OPT_WRIST:
pred_rotmat_body = rot6d_to_rotmat(
pred_pose.reshape(batch_size, -1, 6)
) # .view(batch_size, 24, 3, 3)
if cfg.MODEL.PyMAF.PRED_VIS_H:
pred_vis_hands = None
# if self.full_body_mode or 'hand' in self.bhf_names:
if self.smplx_mode or 'hand' in self.bhf_names:
if 'init_lhand' not in kwargs:
# kwargs['init_lhand'] = self.init_lhand.expand(batch_size, -1)
# init with **right** hand pose
kwargs['init_lhand'] = self.init_rhand.expand(batch_size, -1)
if 'init_rhand' not in kwargs:
kwargs['init_rhand'] = self.init_rhand.expand(batch_size, -1)
pred_lhand, pred_rhand = kwargs['init_lhand'], kwargs['init_rhand']
if cfg.MODEL.MESH_MODEL == 'mano' or cfg.MODEL.PyMAF.OPT_WRIST:
if 'init_orient_rh' not in kwargs:
kwargs['init_orient_rh'] = self.init_orient.expand(batch_size, -1)
if 'init_shape_rh' not in kwargs:
kwargs['init_shape_rh'] = self.init_shape.expand(batch_size, -1)
if 'init_cam_rh' not in kwargs:
kwargs['init_cam_rh'] = self.init_cam.expand(batch_size, -1)
pred_orient_rh = kwargs['init_orient_rh']
pred_shape_rh = kwargs['init_shape_rh']
pred_cam_rh = kwargs['init_cam_rh']
if cfg.MODEL.PyMAF.OPT_WRIST:
if 'init_orient_lh' not in kwargs:
kwargs['init_orient_lh'] = self.init_orient.expand(batch_size, -1)
if 'init_shape_lh' not in kwargs:
kwargs['init_shape_lh'] = self.init_shape.expand(batch_size, -1)
if 'init_cam_lh' not in kwargs:
kwargs['init_cam_lh'] = self.init_cam.expand(batch_size, -1)
pred_orient_lh = kwargs['init_orient_lh']
pred_shape_lh = kwargs['init_shape_lh']
pred_cam_lh = kwargs['init_cam_lh']
if cfg.MODEL.MESH_MODEL == 'mano':
pred_cam = torch.cat([pred_cam_rh[:, 0:1] * 10., pred_cam_rh[:, 1:]], dim=1)
# if self.full_body_mode or 'face' in self.bhf_names:
if self.smplx_mode or 'face' in self.bhf_names:
if 'init_face' not in kwargs:
kwargs['init_face'] = self.init_face.expand(batch_size, -1)
if 'init_hand' not in kwargs:
kwargs['init_exp'] = self.init_exp.expand(batch_size, -1)
pred_face = kwargs['init_face']
pred_exp = kwargs['init_exp']
if cfg.MODEL.MESH_MODEL == 'flame' or cfg.MODEL.PyMAF.OPT_WRIST:
if 'init_orient_fa' not in kwargs:
kwargs['init_orient_fa'] = self.init_orient.expand(batch_size, -1)
pred_orient_fa = kwargs['init_orient_fa']
if 'init_shape_fa' not in kwargs:
kwargs['init_shape_fa'] = self.init_shape.expand(batch_size, -1)
if 'init_cam_fa' not in kwargs:
kwargs['init_cam_fa'] = self.init_cam.expand(batch_size, -1)
pred_shape_fa = kwargs['init_shape_fa']
pred_cam_fa = kwargs['init_cam_fa']
if cfg.MODEL.MESH_MODEL == 'flame':
pred_cam = torch.cat([pred_cam_fa[:, 0:1] * 10., pred_cam_fa[:, 1:]], dim=1)
if not init_mode:
for i in range(n_iter):
if 'body' in self.bhf_names:
xc = torch.cat([x, pred_pose, pred_shape, pred_cam], 1)
if self.use_cam_feats:
if cfg.MODEL.USE_IWP_CAM:
# for IWP camera, simply use pre-defined values
vfov = torch.ones((batch_size, 1)).to(xc) * 0.8
crop_ratio = torch.ones((batch_size, 1)).to(xc) * 0.3
crop_center = torch.ones((batch_size, 2)).to(xc) * 0.5
else:
vfov = rw_cam['vfov'][:, None]
crop_ratio = rw_cam['crop_ratio'][:, None]
crop_center = rw_cam['bbox_center'] / torch.cat(
[rw_cam['img_w'][:, None], rw_cam['img_h'][:, None]], 1
)
xc = torch.cat([xc, vfov, crop_ratio, crop_center], 1)
xc = self.fc1(xc)
xc = self.drop1(xc)
xc = self.fc2(xc)
xc = self.drop2(xc)
pred_cam = self.deccam(xc) + pred_cam
pred_pose = self.decpose(xc) + pred_pose
pred_shape = self.decshape(xc) + pred_shape
if not self.smpl_mode:
if self.hand_only_mode:
xc_rhand = kwargs['xc_rhand']
xc_rhand = torch.cat([xc_rhand, pred_rhand], 1)
elif self.face_only_mode:
xc_face = kwargs['xc_face']
xc_face = torch.cat([xc_face, pred_face, pred_exp], 1)
elif self.body_hand_mode:
xc_lhand, xc_rhand = kwargs['xc_lhand'], kwargs['xc_rhand']
xc_lhand = torch.cat([xc_lhand, pred_lhand], 1)
xc_rhand = torch.cat([xc_rhand, pred_rhand], 1)
elif self.full_body_mode:
xc_lhand, xc_rhand, xc_face = kwargs['xc_lhand'], kwargs['xc_rhand'
], kwargs['xc_face']
xc_lhand = torch.cat([xc_lhand, pred_lhand], 1)
xc_rhand = torch.cat([xc_rhand, pred_rhand], 1)
xc_face = torch.cat([xc_face, pred_face, pred_exp], 1)
if 'lhand' in self.part_names:
xc_lhand = self.drop1_hand(self.fc1_hand(xc_lhand))
xc_lhand = self.drop2_hand(self.fc2_hand(xc_lhand))
pred_lhand = self.decrhand(xc_lhand) + pred_lhand
if cfg.MODEL.PyMAF.OPT_WRIST:
xc_lhand = torch.cat(
[xc_lhand, pred_shape_lh, pred_orient_lh, pred_cam_lh], 1
)
xc_lhand = self.drop3_hand(self.fc3_hand(xc_lhand))
pred_shape_lh = self.decshape_rhand(xc_lhand) + pred_shape_lh
pred_orient_lh = self.decorient_rhand(xc_lhand) + pred_orient_lh
pred_cam_lh = self.deccam_rhand(xc_lhand) + pred_cam_lh
if 'rhand' in self.part_names:
xc_rhand = self.drop1_hand(self.fc1_hand(xc_rhand))
xc_rhand = self.drop2_hand(self.fc2_hand(xc_rhand))
pred_rhand = self.decrhand(xc_rhand) + pred_rhand
if cfg.MODEL.MESH_MODEL == 'mano' or cfg.MODEL.PyMAF.OPT_WRIST:
xc_rhand = torch.cat(
[xc_rhand, pred_shape_rh, pred_orient_rh, pred_cam_rh], 1
)
xc_rhand = self.drop3_hand(self.fc3_hand(xc_rhand))
pred_shape_rh = self.decshape_rhand(xc_rhand) + pred_shape_rh
pred_orient_rh = self.decorient_rhand(xc_rhand) + pred_orient_rh
pred_cam_rh = self.deccam_rhand(xc_rhand) + pred_cam_rh
if cfg.MODEL.MESH_MODEL == 'mano':
pred_cam = torch.cat(
[pred_cam_rh[:, 0:1] * 10., pred_cam_rh[:, 1:] / 10.], dim=1
)
if 'face' in self.part_names:
xc_face = self.drop1_face(self.fc1_face(xc_face))
xc_face = self.drop2_face(self.fc2_face(xc_face))
pred_face = self.dechead(xc_face) + pred_face
pred_exp = self.decexp(xc_face) + pred_exp
if cfg.MODEL.MESH_MODEL == 'flame':
xc_face = torch.cat(
[xc_face, pred_shape_fa, pred_orient_fa, pred_cam_fa], 1
)
xc_face = self.drop3_face(self.fc3_face(xc_face))
pred_shape_fa = self.decshape_face(xc_face) + pred_shape_fa
pred_orient_fa = self.decorient_face(xc_face) + pred_orient_fa
pred_cam_fa = self.deccam_face(xc_face) + pred_cam_fa
if cfg.MODEL.MESH_MODEL == 'flame':
pred_cam = torch.cat(
[pred_cam_fa[:, 0:1] * 10., pred_cam_fa[:, 1:] / 10.], dim=1
)
if self.full_body_mode or self.body_hand_mode:
if cfg.MODEL.PyMAF.PRED_VIS_H:
xc_vis = torch.cat([xc, xc_lhand, xc_rhand], 1)
xc_vis = self.drop1_vis(self.fc1_vis(xc_vis))
xc_vis = self.drop2_vis(self.fc2_vis(xc_vis))
pred_vis_hands = self.decvis(xc_vis)
pred_vis_lhand = pred_vis_hands[:, 0] > cfg.MODEL.PyMAF.HAND_VIS_TH
pred_vis_rhand = pred_vis_hands[:, 1] > cfg.MODEL.PyMAF.HAND_VIS_TH
if cfg.MODEL.PyMAF.OPT_WRIST:
pred_rotmat_body = rot6d_to_rotmat(
pred_pose.reshape(batch_size, -1, 6)
) # .view(batch_size, 24, 3, 3)
pred_lwrist = pred_rotmat_body[:, 20]
pred_rwrist = pred_rotmat_body[:, 21]
pred_gl_body, body_joints = self.body_model.get_global_rotation(
global_orient=pred_rotmat_body[:, 0:1],
body_pose=pred_rotmat_body[:, 1:]
)
pred_gl_lelbow = pred_gl_body[:, 18]
pred_gl_relbow = pred_gl_body[:, 19]
target_gl_lwrist = rot6d_to_rotmat(
pred_orient_lh.reshape(batch_size, -1, 6)
)
target_gl_lwrist *= self.flip_vector.to(target_gl_lwrist.device)
target_gl_rwrist = rot6d_to_rotmat(
pred_orient_rh.reshape(batch_size, -1, 6)
)
opt_lwrist = torch.bmm(pred_gl_lelbow.transpose(1, 2), target_gl_lwrist)
opt_rwrist = torch.bmm(pred_gl_relbow.transpose(1, 2), target_gl_rwrist)
if cfg.MODEL.PyMAF.ADAPT_INTEGR:
# if cfg.MODEL.PyMAF.ADAPT_INTEGR and global_iter == (cfg.MODEL.PyMAF.N_ITER - 1):
tpose_joints = self.smpl.get_tpose(betas=pred_shape)
lelbow_twist_axis = nn.functional.normalize(
tpose_joints[:, 20] - tpose_joints[:, 18], dim=1
)
relbow_twist_axis = nn.functional.normalize(
tpose_joints[:, 21] - tpose_joints[:, 19], dim=1
)
lelbow_twist, lelbow_twist_angle = compute_twist_rotation(
opt_lwrist, lelbow_twist_axis
)
relbow_twist, relbow_twist_angle = compute_twist_rotation(
opt_rwrist, relbow_twist_axis
)
min_angle = -0.4 * float(np.pi)
max_angle = 0.4 * float(np.pi)
lelbow_twist_angle[lelbow_twist_angle == torch.
clamp(lelbow_twist_angle, min_angle, max_angle)
] = 0
relbow_twist_angle[relbow_twist_angle == torch.
clamp(relbow_twist_angle, min_angle, max_angle)
] = 0
lelbow_twist_angle[lelbow_twist_angle > max_angle] -= max_angle
lelbow_twist_angle[lelbow_twist_angle < min_angle] -= min_angle
relbow_twist_angle[relbow_twist_angle > max_angle] -= max_angle
relbow_twist_angle[relbow_twist_angle < min_angle] -= min_angle
lelbow_twist = batch_rodrigues(
lelbow_twist_axis * lelbow_twist_angle
)
relbow_twist = batch_rodrigues(
relbow_twist_axis * relbow_twist_angle
)
opt_lwrist = torch.bmm(lelbow_twist.transpose(1, 2), opt_lwrist)
opt_rwrist = torch.bmm(relbow_twist.transpose(1, 2), opt_rwrist)
# left elbow: 18
opt_lelbow = torch.bmm(pred_rotmat_body[:, 18], lelbow_twist)
# right elbow: 19
opt_relbow = torch.bmm(pred_rotmat_body[:, 19], relbow_twist)
if cfg.MODEL.PyMAF.PRED_VIS_H and global_iter == (
cfg.MODEL.PyMAF.N_ITER - 1
):
opt_lwrist_filtered = [
opt_lwrist[_i]
if pred_vis_lhand[_i] else pred_rotmat_body[_i, 20]
for _i in range(batch_size)
]
opt_rwrist_filtered = [
opt_rwrist[_i]
if pred_vis_rhand[_i] else pred_rotmat_body[_i, 21]
for _i in range(batch_size)
]
opt_lelbow_filtered = [
opt_lelbow[_i]
if pred_vis_lhand[_i] else pred_rotmat_body[_i, 18]
for _i in range(batch_size)
]
opt_relbow_filtered = [
opt_relbow[_i]
if pred_vis_rhand[_i] else pred_rotmat_body[_i, 19]
for _i in range(batch_size)
]
opt_lwrist = torch.stack(opt_lwrist_filtered)
opt_rwrist = torch.stack(opt_rwrist_filtered)
opt_lelbow = torch.stack(opt_lelbow_filtered)
opt_relbow = torch.stack(opt_relbow_filtered)
pred_rotmat_body = torch.cat(
[
pred_rotmat_body[:, :18],
opt_lelbow.unsqueeze(1),
opt_relbow.unsqueeze(1),
opt_lwrist.unsqueeze(1),
opt_rwrist.unsqueeze(1), pred_rotmat_body[:, 22:]
], 1
)
else:
if cfg.MODEL.PyMAF.PRED_VIS_H and global_iter == (
cfg.MODEL.PyMAF.N_ITER - 1
):
opt_lwrist_filtered = [
opt_lwrist[_i]
if pred_vis_lhand[_i] else pred_rotmat_body[_i, 20]
for _i in range(batch_size)
]
opt_rwrist_filtered = [
opt_rwrist[_i]
if pred_vis_rhand[_i] else pred_rotmat_body[_i, 21]
for _i in range(batch_size)
]
opt_lwrist = torch.stack(opt_lwrist_filtered)
opt_rwrist = torch.stack(opt_rwrist_filtered)
pred_rotmat_body = torch.cat(
[
pred_rotmat_body[:, :20],
opt_lwrist.unsqueeze(1),
opt_rwrist.unsqueeze(1), pred_rotmat_body[:, 22:]
], 1
)
if self.hand_only_mode:
pred_rotmat_rh = rot6d_to_rotmat(
torch.cat([pred_orient_rh, pred_rhand], dim=1).reshape(batch_size, -1, 6)
) # .view(batch_size, 16, 3, 3)
assert pred_rotmat_rh.shape[1] == 1 + 15
elif self.face_only_mode:
pred_rotmat_fa = rot6d_to_rotmat(
torch.cat([pred_orient_fa, pred_face], dim=1).reshape(batch_size, -1, 6)
) # .view(batch_size, 16, 3, 3)
assert pred_rotmat_fa.shape[1] == 1 + 3
elif self.full_body_mode or self.body_hand_mode:
if cfg.MODEL.PyMAF.OPT_WRIST:
pred_rotmat = pred_rotmat_body
else:
pred_rotmat = rot6d_to_rotmat(
pred_pose.reshape(batch_size, -1, 6)
) # .view(batch_size, 24, 3, 3)
assert pred_rotmat.shape[1] == 24
else:
pred_rotmat = rot6d_to_rotmat(
pred_pose.reshape(batch_size, -1, 6)
) # .view(batch_size, 24, 3, 3)
assert pred_rotmat.shape[1] == 24
# if self.full_body_mode:
if self.smplx_mode:
if cfg.MODEL.PyMAF.PRED_VIS_H and global_iter == (cfg.MODEL.PyMAF.N_ITER - 1):
pred_lhand_filtered = [
pred_lhand[_i] if pred_vis_lhand[_i] else self.init_rhand[0]
for _i in range(batch_size)
]
pred_rhand_filtered = [
pred_rhand[_i] if pred_vis_rhand[_i] else self.init_rhand[0]
for _i in range(batch_size)
]
pred_lhand_filtered = torch.stack(pred_lhand_filtered)
pred_rhand_filtered = torch.stack(pred_rhand_filtered)
pred_hf6d = torch.cat([pred_lhand_filtered, pred_rhand_filtered, pred_face],
dim=1).reshape(batch_size, -1, 6)
else:
pred_hf6d = torch.cat([pred_lhand, pred_rhand, pred_face],
dim=1).reshape(batch_size, -1, 6)
pred_hfrotmat = rot6d_to_rotmat(pred_hf6d)
assert pred_hfrotmat.shape[1] == (15 * 2 + 3)
# flip left hand pose
pred_lhand_rotmat = pred_hfrotmat[:, :15] * self.flip_vector.to(pred_hfrotmat.device
).unsqueeze(0)
pred_rhand_rotmat = pred_hfrotmat[:, 15:30]
pred_face_rotmat = pred_hfrotmat[:, 30:]
if self.hand_only_mode:
pred_output = self.mano(
betas=pred_shape_rh,
right_hand_pose=pred_rotmat_rh[:, 1:],
global_orient=pred_rotmat_rh[:, 0].unsqueeze(1),
pose2rot=False,
)
elif self.face_only_mode:
pred_output = self.flame(
betas=pred_shape_fa,
global_orient=pred_rotmat_fa[:, 0].unsqueeze(1),
jaw_pose=pred_rotmat_fa[:, 1:2],
leye_pose=pred_rotmat_fa[:, 2:3],
reye_pose=pred_rotmat_fa[:, 3:4],
expression=pred_exp,
pose2rot=False,
)
else:
smplx_kwargs = {}
# if self.full_body_mode:
if self.smplx_mode:
smplx_kwargs['left_hand_pose'] = pred_lhand_rotmat
smplx_kwargs['right_hand_pose'] = pred_rhand_rotmat
smplx_kwargs['jaw_pose'] = pred_face_rotmat[:, 0:1]
smplx_kwargs['leye_pose'] = pred_face_rotmat[:, 1:2]
smplx_kwargs['reye_pose'] = pred_face_rotmat[:, 2:3]
smplx_kwargs['expression'] = pred_exp
pred_output = self.smpl(
betas=pred_shape,
body_pose=pred_rotmat[:, 1:],
global_orient=pred_rotmat[:, 0].unsqueeze(1),
pose2rot=False,
**smplx_kwargs,
)
pred_vertices = pred_output.vertices
pred_joints = pred_output.joints
if self.hand_only_mode:
pred_joints_full = pred_output.rhand_joints
elif self.face_only_mode:
pred_joints_full = pred_output.face_joints
elif self.smplx_mode:
pred_joints_full = torch.cat(
[
pred_joints, pred_output.lhand_joints, pred_output.rhand_joints,
pred_output.face_joints, pred_output.lfoot_joints, pred_output.rfoot_joints
],
dim=1
)
else:
pred_joints_full = pred_joints
pred_keypoints_2d = projection(
pred_joints_full, {
**rw_cam, 'cam_sxy': pred_cam
}, iwp_mode=cfg.MODEL.USE_IWP_CAM
)
if cfg.MODEL.USE_IWP_CAM:
# Normalize keypoints to [-1,1]
pred_keypoints_2d = pred_keypoints_2d / (224. / 2.)
else:
pred_keypoints_2d = j2d_processing(pred_keypoints_2d, rw_cam['kps_transf'])
len_b_kp = len(constants.JOINT_NAMES)
output = {}
if self.smpl_mode or self.smplx_mode:
if J_regressor is not None:
kp_3d = torch.matmul(J_regressor, pred_vertices)
pred_pelvis = kp_3d[:, [0], :].clone()
kp_3d = kp_3d[:, constants.H36M_TO_J14, :]
kp_3d = kp_3d - pred_pelvis
else:
kp_3d = pred_joints
pose = rotation_matrix_to_angle_axis(pred_rotmat.reshape(-1, 3, 3)).reshape(-1, 72)
output.update(
{
'theta': torch.cat([pred_cam, pred_shape, pose], dim=1),
'verts': pred_vertices,
'kp_2d': pred_keypoints_2d[:, :len_b_kp],
'kp_3d': kp_3d,
'pred_joints': pred_joints,
'smpl_kp_3d': pred_output.smpl_joints,
'rotmat': pred_rotmat,
'pred_cam': pred_cam,
'pred_shape': pred_shape,
'pred_pose': pred_pose,
}
)
# if self.full_body_mode:
if self.smplx_mode:
# assert pred_keypoints_2d.shape[1] == 144
len_h_kp = len(constants.HAND_NAMES)
len_f_kp = len(constants.FACIAL_LANDMARKS)
len_feet_kp = 2 * len(constants.FOOT_NAMES)
output.update(
{
'smplx_verts':
pred_output.smplx_vertices if cfg.MODEL.EVAL_MODE else None,
'pred_lhand':
pred_lhand,
'pred_rhand':
pred_rhand,
'pred_face':
pred_face,
'pred_exp':
pred_exp,
'verts_lh':
pred_output.lhand_vertices,
'verts_rh':
pred_output.rhand_vertices,
# 'pred_arm_rotmat': pred_arm_rotmat,
# 'pred_hfrotmat': pred_hfrotmat,
'pred_lhand_rotmat':
pred_lhand_rotmat,
'pred_rhand_rotmat':
pred_rhand_rotmat,
'pred_face_rotmat':
pred_face_rotmat,
'pred_lhand_kp3d':
pred_output.lhand_joints,
'pred_rhand_kp3d':
pred_output.rhand_joints,
'pred_face_kp3d':
pred_output.face_joints,
'pred_lhand_kp2d':
pred_keypoints_2d[:, len_b_kp:len_b_kp + len_h_kp],
'pred_rhand_kp2d':
pred_keypoints_2d[:, len_b_kp + len_h_kp:len_b_kp + len_h_kp * 2],
'pred_face_kp2d':
pred_keypoints_2d[:, len_b_kp + len_h_kp * 2:len_b_kp + len_h_kp * 2 +
len_f_kp],
'pred_feet_kp2d':
pred_keypoints_2d[:, len_b_kp + len_h_kp * 2 + len_f_kp:len_b_kp +
len_h_kp * 2 + len_f_kp + len_feet_kp],
}
)
if cfg.MODEL.PyMAF.OPT_WRIST:
output.update(
{
'pred_orient_lh': pred_orient_lh,
'pred_shape_lh': pred_shape_lh,
'pred_orient_rh': pred_orient_rh,
'pred_shape_rh': pred_shape_rh,
'pred_cam_fa': pred_cam_fa,
'pred_cam_lh': pred_cam_lh,
'pred_cam_rh': pred_cam_rh,
}
)
if cfg.MODEL.PyMAF.PRED_VIS_H:
output.update({'pred_vis_hands': pred_vis_hands})
elif self.hand_only_mode:
# hand mesh out
assert pred_keypoints_2d.shape[1] == 21
output.update(
{
'theta': pred_cam,
'pred_cam': pred_cam,
'pred_rhand': pred_rhand,
'pred_rhand_rotmat': pred_rotmat_rh[:, 1:],
'pred_orient_rh': pred_orient_rh,
'pred_orient_rh_rotmat': pred_rotmat_rh[:, 0],
'verts_rh': pred_output.rhand_vertices,
'pred_cam_rh': pred_cam_rh,
'pred_shape_rh': pred_shape_rh,
'pred_rhand_kp3d': pred_output.rhand_joints,
'pred_rhand_kp2d': pred_keypoints_2d,
}
)
elif self.face_only_mode:
# face mesh out
assert pred_keypoints_2d.shape[1] == 68
output.update(
{
'theta': pred_cam,
'pred_cam': pred_cam,
'pred_face': pred_face,
'pred_exp': pred_exp,
'pred_face_rotmat': pred_rotmat_fa[:, 1:],
'pred_orient_fa': pred_orient_fa,
'pred_orient_fa_rotmat': pred_rotmat_fa[:, 0],
'verts_fa': pred_output.flame_vertices,
'pred_cam_fa': pred_cam_fa,
'pred_shape_fa': pred_shape_fa,
'pred_face_kp3d': pred_output.face_joints,
'pred_face_kp2d': pred_keypoints_2d,
}
)
return output
def get_attention_modules(
module_keys, img_feature_dim_list, hidden_feat_dim, n_iter, num_attention_heads=1
):
align_attention = nn.ModuleDict()
for k in module_keys:
align_attention[k] = nn.ModuleList()
for i in range(n_iter):
align_attention[k].append(
get_att_block(
img_feature_dim=img_feature_dim_list[k][i],
hidden_feat_dim=hidden_feat_dim,
num_attention_heads=num_attention_heads
)
)
return align_attention
def get_fusion_modules(module_keys, ma_feat_dim, grid_feat_dim, n_iter, out_feat_len):
feat_fusion = nn.ModuleDict()
for k in module_keys:
feat_fusion[k] = nn.ModuleList()
for i in range(n_iter):
feat_fusion[k].append(nn.Linear(grid_feat_dim + ma_feat_dim[k], out_feat_len[k]))
return feat_fusion
class PyMAF(nn.Module):
""" PyMAF based Regression Network for Human Mesh Recovery / Full-body Mesh Recovery
PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop, in ICCV, 2021
PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images, arXiv:2207.06400, 2022
"""
def __init__(
self, smpl_mean_params=SMPL_MEAN_PARAMS, pretrained=True, device=torch.device('cuda')
):
super().__init__()
self.device = device
self.smpl_mode = (cfg.MODEL.MESH_MODEL == 'smpl')
self.smplx_mode = (cfg.MODEL.MESH_MODEL == 'smplx')
assert cfg.TRAIN.BHF_MODE in [
'body_only', 'hand_only', 'face_only', 'body_hand', 'full_body'
]
self.hand_only_mode = (cfg.TRAIN.BHF_MODE == 'hand_only')
self.face_only_mode = (cfg.TRAIN.BHF_MODE == 'face_only')
self.body_hand_mode = (cfg.TRAIN.BHF_MODE == 'body_hand')
self.full_body_mode = (cfg.TRAIN.BHF_MODE == 'full_body')
bhf_names = []
if cfg.TRAIN.BHF_MODE in ['body_only', 'body_hand', 'full_body']:
bhf_names.append('body')
if cfg.TRAIN.BHF_MODE in ['hand_only', 'body_hand', 'full_body']:
bhf_names.append('hand')
if cfg.TRAIN.BHF_MODE in ['face_only', 'full_body']:
bhf_names.append('face')
self.bhf_names = bhf_names
self.part_module_names = {'body': {}, 'hand': {}, 'face': {}, 'link': {}}
# the limb parts need to be handled
if self.hand_only_mode:
self.part_names = ['rhand']
elif self.face_only_mode:
self.part_names = ['face']
elif self.body_hand_mode:
self.part_names = ['lhand', 'rhand']
elif self.full_body_mode:
self.part_names = ['lhand', 'rhand', 'face']
else:
self.part_names = []
# joint index info
if not self.smpl_mode:
h_root_idx = constants.HAND_NAMES.index('wrist')
h_idx = constants.HAND_NAMES.index('middle1')
f_idx = constants.FACIAL_LANDMARKS.index('nose_middle')
self.hf_center_idx = {'lhand': h_idx, 'rhand': h_idx, 'face': f_idx}
self.hf_root_idx = {'lhand': h_root_idx, 'rhand': h_root_idx, 'face': f_idx}
lh_idx_coco = constants.COCO_KEYPOINTS.index('left_wrist')
rh_idx_coco = constants.COCO_KEYPOINTS.index('right_wrist')
f_idx_coco = constants.COCO_KEYPOINTS.index('nose')
self.hf_root_idx_coco = {'lhand': lh_idx_coco, 'rhand': rh_idx_coco, 'face': f_idx_coco}
# create parametric mesh models
self.smpl_family = {}
if self.hand_only_mode and cfg.MODEL.MESH_MODEL == 'mano':
self.smpl_family['hand'] = SMPL_Family(model_type='mano')
self.smpl_family['body'] = SMPL_Family(model_type='smplx')
elif self.face_only_mode and cfg.MODEL.MESH_MODEL == 'flame':
self.smpl_family['face'] = SMPL_Family(model_type='flame')
self.smpl_family['body'] = SMPL_Family(model_type='smplx')
else:
self.smpl_family['body'] = SMPL_Family(
model_type=cfg.MODEL.MESH_MODEL, all_gender=cfg.MODEL.ALL_GENDER
)
self.init_mesh_output = None
self.batch_size = 1
self.encoders = nn.ModuleDict()
self.global_mode = not cfg.MODEL.PyMAF.MAF_ON
# build encoders
global_feat_dim = 2048
bhf_ma_feat_dim = {}
# encoder for the body part
if 'body' in bhf_names:
# if self.smplx_mode or 'hr' in cfg.MODEL.PyMAF.BACKBONE:
if cfg.MODEL.PyMAF.BACKBONE == 'res50':
body_encoder = get_resnet_encoder(
cfg, init_weight=(not cfg.MODEL.EVAL_MODE), global_mode=self.global_mode
)
body_sfeat_dim = list(cfg.POSE_RES_MODEL.EXTRA.NUM_DECONV_FILTERS)
elif cfg.MODEL.PyMAF.BACKBONE == 'hr48':
body_encoder = get_hrnet_encoder(
cfg, init_weight=(not cfg.MODEL.EVAL_MODE), global_mode=self.global_mode
)
body_sfeat_dim = list(cfg.HR_MODEL.EXTRA.STAGE4.NUM_CHANNELS)
body_sfeat_dim.reverse()
body_sfeat_dim = body_sfeat_dim[1:]
else:
raise NotImplementedError
self.encoders['body'] = body_encoder
self.part_module_names['body'].update({'encoders.body': self.encoders['body']})
self.mesh_sampler = Mesh_Sampler(type='smpl')
self.part_module_names['body'].update({'mesh_sampler': self.mesh_sampler})
if not cfg.MODEL.PyMAF.GRID_FEAT:
ma_feat_dim = self.mesh_sampler.Dmap.shape[0] * cfg.MODEL.PyMAF.MLP_DIM[-1]
else:
ma_feat_dim = 0
bhf_ma_feat_dim['body'] = ma_feat_dim
dp_feat_dim = body_sfeat_dim[-1]
self.with_uv = cfg.LOSS.POINT_REGRESSION_WEIGHTS > 0
if cfg.MODEL.PyMAF.AUX_SUPV_ON:
assert cfg.MODEL.PyMAF.MAF_ON
self.dp_head = IUV_predict_layer(feat_dim=dp_feat_dim)
self.part_module_names['body'].update({'dp_head': self.dp_head})
# encoders for the hand / face parts
if 'hand' in self.bhf_names or 'face' in self.bhf_names:
for hf in ['hand', 'face']:
if hf in bhf_names:
if cfg.MODEL.PyMAF.HF_BACKBONE == 'res50':
self.encoders[hf] = get_resnet_encoder(
cfg,
init_weight=(not cfg.MODEL.EVAL_MODE),
global_mode=self.global_mode
)
self.part_module_names[hf].update({f'encoders.{hf}': self.encoders[hf]})
hf_sfeat_dim = list(cfg.POSE_RES_MODEL.EXTRA.NUM_DECONV_FILTERS)
else:
raise NotImplementedError
if cfg.MODEL.PyMAF.HF_AUX_SUPV_ON:
assert cfg.MODEL.PyMAF.MAF_ON
self.dp_head_hf = nn.ModuleDict()
if 'hand' in bhf_names:
self.dp_head_hf['hand'] = IUV_predict_layer(
feat_dim=hf_sfeat_dim[-1], mode='pncc'
)
self.part_module_names['hand'].update(
{'dp_head_hf.hand': self.dp_head_hf['hand']}
)
if 'face' in bhf_names:
self.dp_head_hf['face'] = IUV_predict_layer(
feat_dim=hf_sfeat_dim[-1], mode='pncc'
)
self.part_module_names['face'].update(
{'dp_head_hf.face': self.dp_head_hf['face']}
)
smpl2limb_vert_faces = get_partial_smpl()
self.smpl2lhand = torch.from_numpy(smpl2limb_vert_faces['lhand']['vids']).long()
self.smpl2rhand = torch.from_numpy(smpl2limb_vert_faces['rhand']['vids']).long()
# grid points for grid feature extraction
grid_size = 21
xv, yv = torch.meshgrid(
[torch.linspace(-1, 1, grid_size),
torch.linspace(-1, 1, grid_size)]
)
grid_points = torch.stack([xv.reshape(-1), yv.reshape(-1)]).unsqueeze(0)
self.register_buffer('grid_points', grid_points)
grid_feat_dim = grid_size * grid_size * cfg.MODEL.PyMAF.MLP_DIM[-1]
# the fusion of grid and mesh-aligned features
self.fuse_grid_align = cfg.MODEL.PyMAF.GRID_ALIGN.USE_ATT or cfg.MODEL.PyMAF.GRID_ALIGN.USE_FC
assert not (cfg.MODEL.PyMAF.GRID_ALIGN.USE_ATT and cfg.MODEL.PyMAF.GRID_ALIGN.USE_FC)
if self.fuse_grid_align:
self.att_starts = cfg.MODEL.PyMAF.GRID_ALIGN.ATT_STARTS
n_iter_att = cfg.MODEL.PyMAF.N_ITER - self.att_starts
att_feat_dim_idx = -cfg.MODEL.PyMAF.GRID_ALIGN.ATT_FEAT_IDX
num_att_heads = cfg.MODEL.PyMAF.GRID_ALIGN.ATT_HEAD
hidden_feat_dim = cfg.MODEL.PyMAF.MLP_DIM[att_feat_dim_idx]
bhf_att_feat_dim = {'body': 2048}
if 'hand' in self.bhf_names:
self.mano_sampler = Mesh_Sampler(type='mano', level=1)
self.mano_ds_len = self.mano_sampler.Dmap.shape[0]
self.part_module_names['hand'].update({'mano_sampler': self.mano_sampler})
bhf_ma_feat_dim.update({'hand': self.mano_ds_len * cfg.MODEL.PyMAF.HF_MLP_DIM[-1]})
if self.fuse_grid_align:
bhf_att_feat_dim.update({'hand': 1024})
if 'face' in self.bhf_names:
bhf_ma_feat_dim.update(
{'face': len(constants.FACIAL_LANDMARKS) * cfg.MODEL.PyMAF.HF_MLP_DIM[-1]}
)
if self.fuse_grid_align:
bhf_att_feat_dim.update({'face': 1024})
# spatial alignment attention
if cfg.MODEL.PyMAF.GRID_ALIGN.USE_ATT:
hfimg_feat_dim_list = {}
if 'body' in bhf_names:
hfimg_feat_dim_list['body'] = body_sfeat_dim[-n_iter_att:]
if 'hand' in self.bhf_names or 'face' in self.bhf_names:
if 'hand' in bhf_names:
hfimg_feat_dim_list['hand'] = hf_sfeat_dim[-n_iter_att:]
if 'face' in bhf_names:
hfimg_feat_dim_list['face'] = hf_sfeat_dim[-n_iter_att:]
self.align_attention = get_attention_modules(
bhf_names,
hfimg_feat_dim_list,
hidden_feat_dim,
n_iter=n_iter_att,
num_attention_heads=num_att_heads
)
for part in bhf_names:
self.part_module_names[part].update(
{f'align_attention.{part}': self.align_attention[part]}
)
if self.fuse_grid_align:
self.att_feat_reduce = get_fusion_modules(
bhf_names,
bhf_ma_feat_dim,
grid_feat_dim,
n_iter=n_iter_att,
out_feat_len=bhf_att_feat_dim
)
for part in bhf_names:
self.part_module_names[part].update(
{f'att_feat_reduce.{part}': self.att_feat_reduce[part]}
)
# build regressor for parameter prediction
self.regressor = nn.ModuleList()
for i in range(cfg.MODEL.PyMAF.N_ITER):
ref_infeat_dim = 0
if 'body' in self.bhf_names:
if cfg.MODEL.PyMAF.MAF_ON:
if self.fuse_grid_align:
if i >= self.att_starts:
ref_infeat_dim = bhf_att_feat_dim['body']
elif i == 0 or cfg.MODEL.PyMAF.GRID_FEAT:
ref_infeat_dim = grid_feat_dim
else:
ref_infeat_dim = ma_feat_dim
else:
if i == 0 or cfg.MODEL.PyMAF.GRID_FEAT:
ref_infeat_dim = grid_feat_dim
else:
ref_infeat_dim = ma_feat_dim
else:
ref_infeat_dim = global_feat_dim
if self.smpl_mode:
self.regressor.append(
Regressor(
feat_dim=ref_infeat_dim,
smpl_mean_params=smpl_mean_params,
use_cam_feats=cfg.MODEL.PyMAF.USE_CAM_FEAT,
smpl_models=self.smpl_family
)
)
else:
if cfg.MODEL.PyMAF.MAF_ON:
if 'hand' in self.bhf_names or 'face' in self.bhf_names:
if i == 0:
feat_dim_hand = grid_feat_dim if 'hand' in self.bhf_names else None
feat_dim_face = grid_feat_dim if 'face' in self.bhf_names else None
else:
if self.fuse_grid_align:
feat_dim_hand = bhf_att_feat_dim[
'hand'] if 'hand' in self.bhf_names else None
feat_dim_face = bhf_att_feat_dim[
'face'] if 'face' in self.bhf_names else None
else:
feat_dim_hand = bhf_ma_feat_dim[
'hand'] if 'hand' in self.bhf_names else None
feat_dim_face = bhf_ma_feat_dim[
'face'] if 'face' in self.bhf_names else None
else:
feat_dim_hand = ref_infeat_dim
feat_dim_face = ref_infeat_dim
else:
ref_infeat_dim = global_feat_dim
feat_dim_hand = global_feat_dim
feat_dim_face = global_feat_dim
self.regressor.append(
Regressor(
feat_dim=ref_infeat_dim,
smpl_mean_params=smpl_mean_params,
use_cam_feats=cfg.MODEL.PyMAF.USE_CAM_FEAT,
feat_dim_hand=feat_dim_hand,
feat_dim_face=feat_dim_face,
bhf_names=bhf_names,
smpl_models=self.smpl_family
)
)
# assign sub-regressor to each part
for dec_name, dec_module in self.regressor[-1].named_children():
if 'hand' in dec_name:
self.part_module_names['hand'].update(
{'regressor.{}.{}.'.format(len(self.regressor) - 1, dec_name): dec_module}
)
elif 'face' in dec_name or 'head' in dec_name or 'exp' in dec_name:
self.part_module_names['face'].update(
{'regressor.{}.{}.'.format(len(self.regressor) - 1, dec_name): dec_module}
)
elif 'res' in dec_name or 'vis' in dec_name:
self.part_module_names['link'].update(
{'regressor.{}.{}.'.format(len(self.regressor) - 1, dec_name): dec_module}
)
elif 'body' in self.part_module_names:
self.part_module_names['body'].update(
{'regressor.{}.{}.'.format(len(self.regressor) - 1, dec_name): dec_module}
)
# mesh-aligned feature extractor
self.maf_extractor = nn.ModuleDict()
for part in bhf_names:
self.maf_extractor[part] = nn.ModuleList()
filter_channels_default = cfg.MODEL.PyMAF.MLP_DIM if part == 'body' else cfg.MODEL.PyMAF.HF_MLP_DIM
sfeat_dim = body_sfeat_dim if part == 'body' else hf_sfeat_dim
for i in range(cfg.MODEL.PyMAF.N_ITER):
for f_i, f_dim in enumerate(filter_channels_default):
if sfeat_dim[i] > f_dim:
filter_start = f_i
break
filter_channels = [sfeat_dim[i]] + filter_channels_default[filter_start:]
if cfg.MODEL.PyMAF.GRID_ALIGN.USE_ATT and i >= self.att_starts:
self.maf_extractor[part].append(
MAF_Extractor(
filter_channels=filter_channels_default[att_feat_dim_idx:],
iwp_cam_mode=cfg.MODEL.USE_IWP_CAM
)
)
else:
self.maf_extractor[part].append(
MAF_Extractor(
filter_channels=filter_channels, iwp_cam_mode=cfg.MODEL.USE_IWP_CAM
)
)
self.part_module_names[part].update({f'maf_extractor.{part}': self.maf_extractor[part]})
# check all modules have been added to part_module_names
model_dict_all = dict.fromkeys(self.state_dict().keys())
for key in self.part_module_names.keys():
for name in list(model_dict_all.keys()):
for k in self.part_module_names[key].keys():
if name.startswith(k):
del model_dict_all[name]
# if name.startswith('regressor.') and '.smpl.' in name:
# del model_dict_all[name]
# if name.startswith('regressor.') and '.mano.' in name:
# del model_dict_all[name]
if name.startswith('regressor.') and '.init_' in name:
del model_dict_all[name]
if name == 'grid_points':
del model_dict_all[name]
assert (len(model_dict_all.keys()) == 0)
def init_mesh(self, batch_size, J_regressor=None, rw_cam={}):
""" initialize the mesh model with default poses and shapes
"""
if self.init_mesh_output is None or self.batch_size != batch_size:
self.init_mesh_output = self.regressor[0](
torch.zeros(batch_size), J_regressor=J_regressor, rw_cam=rw_cam, init_mode=True
)
self.batch_size = batch_size
return self.init_mesh_output
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(
self.inplanes,
planes * block.expansion,
kernel_size=1,
stride=stride,
bias=False
),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _make_deconv_layer(self, num_layers, num_filters, num_kernels):
"""
Deconv_layer used in Simple Baselines:
Xiao et al. Simple Baselines for Human Pose Estimation and Tracking
https://github.com/microsoft/human-pose-estimation.pytorch
"""
assert num_layers == len(num_filters), \
'ERROR: num_deconv_layers is different len(num_deconv_filters)'
assert num_layers == len(num_kernels), \
'ERROR: num_deconv_layers is different len(num_deconv_filters)'
def _get_deconv_cfg(deconv_kernel, index):
if deconv_kernel == 4:
padding = 1
output_padding = 0
elif deconv_kernel == 3:
padding = 1
output_padding = 1
elif deconv_kernel == 2:
padding = 0
output_padding = 0
return deconv_kernel, padding, output_padding
layers = []
for i in range(num_layers):
kernel, padding, output_padding = _get_deconv_cfg(num_kernels[i], i)
planes = num_filters[i]
layers.append(
nn.ConvTranspose2d(
in_channels=self.inplanes,
out_channels=planes,
kernel_size=kernel,
stride=2,
padding=padding,
output_padding=output_padding,
bias=self.deconv_with_bias
)
)
layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM))
layers.append(nn.ReLU(inplace=True))
self.inplanes = planes
return nn.Sequential(*layers)
def to(self, *args, **kwargs):
super().to(*args, **kwargs)
for m in ['body', 'hand', 'face']:
if m in self.smpl_family:
self.smpl_family[m].model.to(*args, **kwargs)
return self
def cuda(self, *args, **kwargs):
super().cuda(*args, **kwargs)
for m in ['body', 'hand', 'face']:
if m in self.smpl_family:
self.smpl_family[m].model.cuda(*args, **kwargs)
return self
def forward(self, batch={}, J_regressor=None, rw_cam={}):
'''
Args:
batch: input dictionary, including
images: 'img_{part}', for part in body, hand, and face if applicable
inversed affine transformation for the cropping of hand/face images: '{part}_theta_inv' for part in lhand, rhand, and face if applicable
J_regressor: joint regression matrix
rw_cam: real-world camera information, applied when cfg.MODEL.USE_IWP_CAM is False
Returns:
out_dict: the list containing the predicted parameters
vis_feat_list: the list containing features for visualization
'''
# batch keys: ['img_body', 'orig_height', 'orig_width', 'person_id', 'img_lhand',
# 'lhand_theta_inv', 'img_rhand', 'rhand_theta_inv', 'img_face', 'face_theta_inv']
# extract spatial features or global features
# run encoder for body
if 'body' in self.bhf_names:
img_body = batch['img_body']
batch_size = img_body.shape[0]
s_feat_body, g_feat = self.encoders['body'](batch['img_body'])
if cfg.MODEL.PyMAF.MAF_ON:
assert len(s_feat_body) == cfg.MODEL.PyMAF.N_ITER
# run encoders for hand / face
if 'hand' in self.bhf_names or 'face' in self.bhf_names:
limb_feat_dict = {}
limb_gfeat_dict = {}
if 'face' in self.bhf_names:
img_face = batch['img_face']
batch_size = img_face.shape[0]
limb_feat_dict['face'], limb_gfeat_dict['face'] = self.encoders['face'](img_face)
if 'hand' in self.bhf_names:
if 'lhand' in self.part_names:
img_rhand = batch['img_rhand']
batch_size = img_rhand.shape[0]
# flip left hand images
img_lhand = torch.flip(batch['img_lhand'], [3])
img_hands = torch.cat([img_rhand, img_lhand])
s_feat_hands, g_feat_hands = self.encoders['hand'](img_hands)
limb_feat_dict['rhand'] = [feat[:batch_size] for feat in s_feat_hands]
limb_feat_dict['lhand'] = [feat[batch_size:] for feat in s_feat_hands]
if g_feat_hands is not None:
limb_gfeat_dict['rhand'] = g_feat_hands[:batch_size]
limb_gfeat_dict['lhand'] = g_feat_hands[batch_size:]
else:
img_rhand = batch['img_rhand']
batch_size = img_rhand.shape[0]
limb_feat_dict['rhand'], limb_gfeat_dict['rhand'] = self.encoders['hand'](
img_rhand
)
if cfg.MODEL.PyMAF.MAF_ON:
for k in limb_feat_dict.keys():
assert len(limb_feat_dict[k]) == cfg.MODEL.PyMAF.N_ITER
out_dict = {}
# grid-pattern points
grid_points = torch.transpose(self.grid_points.expand(batch_size, -1, -1), 1, 2)
# initial parameters
mesh_output = self.init_mesh(batch_size, J_regressor, rw_cam)
out_dict['mesh_out'] = [mesh_output]
out_dict['dp_out'] = []
# for visulization
vis_feat_list = []
# dense prediction during training
if not cfg.MODEL.EVAL_MODE:
if 'body' in self.bhf_names:
if cfg.MODEL.PyMAF.AUX_SUPV_ON:
iuv_out_dict = self.dp_head(s_feat_body[-1])
out_dict['dp_out'].append(iuv_out_dict)
elif self.hand_only_mode:
if cfg.MODEL.PyMAF.HF_AUX_SUPV_ON:
out_dict['rhand_dpout'] = []
dphand_out_dict = self.dp_head_hf['hand'](limb_feat_dict['rhand'][-1])
out_dict['rhand_dpout'].append(dphand_out_dict)
elif self.face_only_mode:
if cfg.MODEL.PyMAF.HF_AUX_SUPV_ON:
out_dict['face_dpout'] = []
dpface_out_dict = self.dp_head_hf['face'](limb_feat_dict['face'][-1])
out_dict['face_dpout'].append(dpface_out_dict)
# parameter predictions
for rf_i in range(cfg.MODEL.PyMAF.N_ITER):
current_states = {}
if 'body' in self.bhf_names:
pred_cam = mesh_output['pred_cam'].detach()
pred_shape = mesh_output['pred_shape'].detach()
pred_pose = mesh_output['pred_pose'].detach()
current_states['init_cam'] = pred_cam
current_states['init_shape'] = pred_shape
current_states['init_pose'] = pred_pose
pred_smpl_verts = mesh_output['verts'].detach()
if cfg.MODEL.PyMAF.MAF_ON:
s_feat_i = s_feat_body[rf_i]
# re-project mesh on the image plane
if self.hand_only_mode:
pred_cam = mesh_output['pred_cam'].detach()
pred_rhand_v = self.mano_sampler(mesh_output['verts_rh'])
pred_rhand_proj = projection(
pred_rhand_v, {
**rw_cam, 'cam_sxy': pred_cam
}, iwp_mode=cfg.MODEL.USE_IWP_CAM
)
if cfg.MODEL.USE_IWP_CAM:
pred_rhand_proj = pred_rhand_proj / (224. / 2.)
else:
pred_rhand_proj = j2d_processing(pred_rhand_proj, rw_cam['kps_transf'])
proj_hf_center = {
'rhand':
mesh_output['pred_rhand_kp2d'][:, self.hf_root_idx['rhand']].unsqueeze(1)
}
proj_hf_pts = {
'rhand': torch.cat([proj_hf_center['rhand'], pred_rhand_proj], dim=1)
}
elif self.face_only_mode:
pred_cam = mesh_output['pred_cam'].detach()
pred_face_v = mesh_output['pred_face_kp3d']
pred_face_proj = projection(
pred_face_v, {
**rw_cam, 'cam_sxy': pred_cam
}, iwp_mode=cfg.MODEL.USE_IWP_CAM
)
if cfg.MODEL.USE_IWP_CAM:
pred_face_proj = pred_face_proj / (224. / 2.)
else:
pred_face_proj = j2d_processing(pred_face_proj, rw_cam['kps_transf'])
proj_hf_center = {
'face': mesh_output['pred_face_kp2d'][:, self.hf_root_idx['face']].unsqueeze(1)
}
proj_hf_pts = {'face': torch.cat([proj_hf_center['face'], pred_face_proj], dim=1)}
elif self.body_hand_mode:
pred_lhand_v = self.mano_sampler(pred_smpl_verts[:, self.smpl2lhand])
pred_rhand_v = self.mano_sampler(pred_smpl_verts[:, self.smpl2rhand])
pred_hand_v = torch.cat([pred_lhand_v, pred_rhand_v], dim=1)
pred_hand_proj = projection(
pred_hand_v, {
**rw_cam, 'cam_sxy': pred_cam
}, iwp_mode=cfg.MODEL.USE_IWP_CAM
)
if cfg.MODEL.USE_IWP_CAM:
pred_hand_proj = pred_hand_proj / (224. / 2.)
else:
pred_hand_proj = j2d_processing(pred_hand_proj, rw_cam['kps_transf'])
proj_hf_center = {
'lhand':
mesh_output['pred_lhand_kp2d'][:, self.hf_root_idx['lhand']].unsqueeze(1),
'rhand':
mesh_output['pred_rhand_kp2d'][:, self.hf_root_idx['rhand']].unsqueeze(1),
}
proj_hf_pts = {
'lhand':
torch.cat(
[proj_hf_center['lhand'], pred_hand_proj[:, :self.mano_ds_len]], dim=1
),
'rhand':
torch.cat(
[proj_hf_center['rhand'], pred_hand_proj[:, self.mano_ds_len:]], dim=1
),
}
elif self.full_body_mode:
pred_lhand_v = self.mano_sampler(pred_smpl_verts[:, self.smpl2lhand])
pred_rhand_v = self.mano_sampler(pred_smpl_verts[:, self.smpl2rhand])
pred_hand_v = torch.cat([pred_lhand_v, pred_rhand_v], dim=1)
pred_hand_proj = projection(
pred_hand_v, {
**rw_cam, 'cam_sxy': pred_cam
}, iwp_mode=cfg.MODEL.USE_IWP_CAM
)
if cfg.MODEL.USE_IWP_CAM:
pred_hand_proj = pred_hand_proj / (224. / 2.)
else:
pred_hand_proj = j2d_processing(pred_hand_proj, rw_cam['kps_transf'])
proj_hf_center = {
'lhand':
mesh_output['pred_lhand_kp2d'][:, self.hf_root_idx['lhand']].unsqueeze(1),
'rhand':
mesh_output['pred_rhand_kp2d'][:, self.hf_root_idx['rhand']].unsqueeze(1),
'face':
mesh_output['pred_face_kp2d'][:, self.hf_root_idx['face']].unsqueeze(1)
}
proj_hf_pts = {
'lhand':
torch.cat(
[proj_hf_center['lhand'], pred_hand_proj[:, :self.mano_ds_len]], dim=1
),
'rhand':
torch.cat(
[proj_hf_center['rhand'], pred_hand_proj[:, self.mano_ds_len:]], dim=1
),
'face':
torch.cat([proj_hf_center['face'], mesh_output['pred_face_kp2d']], dim=1)
}
# extract mesh-aligned features for the hand / face part
if 'hand' in self.bhf_names or 'face' in self.bhf_names:
limb_rf_i = rf_i
hand_face_feat = {}
for hf_i, part_name in enumerate(self.part_names):
if 'hand' in part_name:
hf_key = 'hand'
elif 'face' in part_name:
hf_key = 'face'
if cfg.MODEL.PyMAF.MAF_ON:
if cfg.MODEL.PyMAF.HF_BACKBONE == 'res50':
limb_feat_i = limb_feat_dict[part_name][limb_rf_i]
else:
raise NotImplementedError
limb_reduce_dim = (not self.fuse_grid_align) or (rf_i < self.att_starts)
if limb_rf_i == 0 or cfg.MODEL.PyMAF.GRID_FEAT:
limb_ref_feat_ctd = self.maf_extractor[hf_key][limb_rf_i].sampling(
grid_points, im_feat=limb_feat_i, reduce_dim=limb_reduce_dim
)
else:
if self.hand_only_mode or self.face_only_mode:
proj_hf_pts_crop = proj_hf_pts[part_name][:, :, :2]
proj_hf_v_center = proj_hf_pts_crop[:, 0].unsqueeze(1)
if cfg.MODEL.PyMAF.HF_BOX_CENTER:
part_box_ul = torch.min(proj_hf_pts_crop, dim=1)[0].unsqueeze(1)
part_box_br = torch.max(proj_hf_pts_crop, dim=1)[0].unsqueeze(1)
part_box_center = (part_box_ul + part_box_br) / 2.
proj_hf_pts_crop_ctd = proj_hf_pts_crop[:, 1:] - part_box_center
else:
proj_hf_pts_crop_ctd = proj_hf_pts_crop[:, 1:]
elif self.full_body_mode or self.body_hand_mode:
# convert projection points to the space of cropped hand/face images
theta_i_inv = batch[f'{part_name}_theta_inv']
proj_hf_pts_crop = torch.bmm(
theta_i_inv,
homo_vector(proj_hf_pts[part_name][:, :, :2]).permute(0, 2, 1)
).permute(0, 2, 1)
if part_name == 'lhand':
flip_x = torch.tensor([-1, 1])[None,
None, :].to(proj_hf_pts_crop)
proj_hf_pts_crop *= flip_x
if cfg.MODEL.PyMAF.HF_BOX_CENTER:
# align projection points with the cropped image center
part_box_ul = torch.min(proj_hf_pts_crop, dim=1)[0].unsqueeze(1)
part_box_br = torch.max(proj_hf_pts_crop, dim=1)[0].unsqueeze(1)
part_box_center = (part_box_ul + part_box_br) / 2.
proj_hf_pts_crop_ctd = proj_hf_pts_crop[:, 1:] - part_box_center
else:
proj_hf_pts_crop_ctd = proj_hf_pts_crop[:, 1:]
# 0 is the root point
proj_hf_v_center = proj_hf_pts_crop[:, 0].unsqueeze(1)
limb_ref_feat_ctd = self.maf_extractor[hf_key][limb_rf_i].sampling(
proj_hf_pts_crop_ctd.detach(),
im_feat=limb_feat_i,
reduce_dim=limb_reduce_dim
)
if self.fuse_grid_align and limb_rf_i >= self.att_starts:
limb_grid_feature_ctd = self.maf_extractor[hf_key][limb_rf_i].sampling(
grid_points, im_feat=limb_feat_i, reduce_dim=limb_reduce_dim
)
limb_grid_ref_feat_ctd = torch.cat(
[limb_grid_feature_ctd, limb_ref_feat_ctd], dim=-1
).permute(0, 2, 1)
if cfg.MODEL.PyMAF.GRID_ALIGN.USE_ATT:
att_ref_feat_ctd = self.align_attention[hf_key][
limb_rf_i - self.att_starts](limb_grid_ref_feat_ctd)[0]
elif cfg.MODEL.PyMAF.GRID_ALIGN.USE_FC:
att_ref_feat_ctd = limb_grid_ref_feat_ctd
att_ref_feat_ctd = self.maf_extractor[hf_key][limb_rf_i].reduce_dim(
att_ref_feat_ctd.permute(0, 2, 1)
).view(batch_size, -1)
limb_ref_feat_ctd = self.att_feat_reduce[hf_key][
limb_rf_i - self.att_starts](att_ref_feat_ctd)
else:
# limb_ref_feat = limb_ref_feat.view(batch_size, -1)
limb_ref_feat_ctd = limb_ref_feat_ctd.view(batch_size, -1)
hand_face_feat[part_name] = limb_ref_feat_ctd
else:
hand_face_feat[part_name] = limb_gfeat_dict[part_name]
# extract mesh-aligned features for the body part
if 'body' in self.bhf_names:
if cfg.MODEL.PyMAF.MAF_ON:
reduce_dim = (not self.fuse_grid_align) or (rf_i < self.att_starts)
if rf_i == 0 or cfg.MODEL.PyMAF.GRID_FEAT:
ref_feature = self.maf_extractor['body'][rf_i].sampling(
grid_points, im_feat=s_feat_i, reduce_dim=reduce_dim
)
else:
# TODO: use a more sparse SMPL implementation (with 431 vertices) for acceleration
pred_smpl_verts_ds = self.mesh_sampler.downsample(
pred_smpl_verts
) # [B, 431, 3]
ref_feature = self.maf_extractor['body'][rf_i](
pred_smpl_verts_ds,
im_feat=s_feat_i,
cam={
**rw_cam, 'cam_sxy': pred_cam
},
add_att=True,
reduce_dim=reduce_dim
) # [B, 431 * n_feat]
if self.fuse_grid_align and rf_i >= self.att_starts:
if rf_i > 0 and not cfg.MODEL.PyMAF.GRID_FEAT:
grid_feature = self.maf_extractor['body'][rf_i].sampling(
grid_points, im_feat=s_feat_i, reduce_dim=reduce_dim
)
grid_ref_feat = torch.cat([grid_feature, ref_feature], dim=-1)
else:
grid_ref_feat = ref_feature
grid_ref_feat = grid_ref_feat.permute(0, 2, 1)
if cfg.MODEL.PyMAF.GRID_ALIGN.USE_ATT:
att_ref_feat = self.align_attention['body'][
rf_i - self.att_starts](grid_ref_feat)[0]
elif cfg.MODEL.PyMAF.GRID_ALIGN.USE_FC:
att_ref_feat = grid_ref_feat
att_ref_feat = self.maf_extractor['body'][rf_i].reduce_dim(
att_ref_feat.permute(0, 2, 1)
)
att_ref_feat = att_ref_feat.view(batch_size, -1)
ref_feature = self.att_feat_reduce['body'][rf_i -
self.att_starts](att_ref_feat)
else:
ref_feature = ref_feature.view(batch_size, -1)
else:
ref_feature = g_feat
else:
ref_feature = None
if not self.smpl_mode:
if self.hand_only_mode:
current_states['xc_rhand'] = hand_face_feat['rhand']
elif self.face_only_mode:
current_states['xc_face'] = hand_face_feat['face']
elif self.body_hand_mode:
current_states['xc_lhand'] = hand_face_feat['lhand']
current_states['xc_rhand'] = hand_face_feat['rhand']
elif self.full_body_mode:
current_states['xc_lhand'] = hand_face_feat['lhand']
current_states['xc_rhand'] = hand_face_feat['rhand']
current_states['xc_face'] = hand_face_feat['face']
if rf_i > 0:
for part in self.part_names:
current_states[f'init_{part}'] = mesh_output[f'pred_{part}'].detach()
if part == 'face':
current_states['init_exp'] = mesh_output['pred_exp'].detach()
if self.hand_only_mode:
current_states['init_shape_rh'] = mesh_output['pred_shape_rh'].detach()
current_states['init_orient_rh'] = mesh_output['pred_orient_rh'].detach()
current_states['init_cam_rh'] = mesh_output['pred_cam_rh'].detach()
elif self.face_only_mode:
current_states['init_shape_fa'] = mesh_output['pred_shape_fa'].detach()
current_states['init_orient_fa'] = mesh_output['pred_orient_fa'].detach()
current_states['init_cam_fa'] = mesh_output['pred_cam_fa'].detach()
elif self.full_body_mode or self.body_hand_mode:
if cfg.MODEL.PyMAF.OPT_WRIST:
current_states['init_shape_lh'] = mesh_output['pred_shape_lh'].detach()
current_states['init_orient_lh'] = mesh_output['pred_orient_lh'].detach(
)
current_states['init_cam_lh'] = mesh_output['pred_cam_lh'].detach()
current_states['init_shape_rh'] = mesh_output['pred_shape_rh'].detach()
current_states['init_orient_rh'] = mesh_output['pred_orient_rh'].detach(
)
current_states['init_cam_rh'] = mesh_output['pred_cam_rh'].detach()
# update mesh parameters
mesh_output = self.regressor[rf_i](
ref_feature,
n_iter=1,
J_regressor=J_regressor,
rw_cam=rw_cam,
global_iter=rf_i,
**current_states
)
out_dict['mesh_out'].append(mesh_output)
return out_dict, vis_feat_list
def pymaf_net(smpl_mean_params, pretrained=True, device=torch.device('cuda')):
""" Constructs an PyMAF model with ResNet50 backbone.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = PyMAF(smpl_mean_params, pretrained, device)
return model
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