def get_landmarks(self):
"""Computes body25 joints for SMPL along with hand and facial landmarks"""
verts, _, _, _ = self.smpl(self.pose,
th_betas=self.betas,
th_trans=self.trans,
th_offsets=self.offsets)
J = batch_sparse_dense_matmul(self.body25_reg_torch, verts)
face = batch_sparse_dense_matmul(self.face_reg_torch, verts)
hands = batch_sparse_dense_matmul(self.hand_reg_torch, verts)
return J, face, hands
def get_pose_obj(pose_3d, smpl):
# Bharat: Why do we have torch, chumpy and numpy in the same function. It seems all the ops can be done in torch.
body25_reg_torch, face_reg_torch, hand_reg_torch = torch_pose_obj_data()
ipdb.set_trace()
verts, _, _, _ = smpl.forward()
J = batch_sparse_dense_matmul(body25_reg_torch, verts)
face = batch_sparse_dense_matmul(face_reg_torch, verts)
hands = batch_sparse_dense_matmul(hand_reg_torch, verts)
J_observed = pose_3d['pose_keypoints_3d']
face_observed = pose_3d['face_keypoints_3d']
hands_observed = np.vstack((pose_3d['hand_left_keypoints_3d'], pose_3d['hand_right_keypoints_3d']))
hands_observed[:, 3][hands_observed[:, 3] < HAND_VISIBLE] = 0.
#
return ch.vstack((
(J - J_observed[:, :3]) * J_observed[:, 3].reshape((-1, 1)),
(face - face_observed[:, :3]) * face_observed[:, 3].reshape((-1, 1)),
(hands - hands_observed[:, :3]) * hands_observed[:, 3].reshape((-1, 1)),
))