def forward(self, *args, **kwargs):
kwargs['get_skin'] = True
smpl_output = super(SMPL, self).forward(*args, **kwargs)
extra_joints = vertices2joints(self.J_regressor_extra, smpl_output.vertices)
h36m_joints = vertices2joints(self.J_regressor_h36m, smpl_output.vertices)
all_joints = torch.cat([smpl_output.joints, extra_joints, h36m_joints], dim=1)
output = ModelOutput(vertices=smpl_output.vertices,
global_orient=smpl_output.global_orient,
body_pose=smpl_output.body_pose,
joints=all_joints,
betas=smpl_output.betas,
full_pose=smpl_output.full_pose)
return output
def get_default_pose(v_template, betas, shapedirs, J_regressor):
from smplx.lbs import vertices2joints, blend_shapes
v_shaped = v_template + blend_shapes(betas, shapedirs)
# Get the joints
# NxJx3 array
J = vertices2joints(J_regressor, v_shaped)
return c2c(J[0])
def forward(self, *args, **kwargs):
kwargs['get_skin'] = True
#if pose parameter is for SMPL with 21 joints (ignoring root)
if (kwargs['body_pose'].shape[1] == 69):
kwargs['body_pose'] = kwargs[
'body_pose'][:, :-2 *
3] #Ignore the last two joints (which are on the palm. Not used)
if (kwargs['body_pose'].shape[1] == 23):
kwargs['body_pose'] = kwargs[
'body_pose'][:, :
-2] #Ignore the last two joints (which are on the palm. Not used)
smpl_output = super(SMPLX, self).forward(*args, **kwargs)
extra_joints = vertices2joints(self.J_regressor_extra,
smpl_output.vertices)
# extra_joints = vertices2joints(self.J_regressor_extra, smpl_output.vertices[:,:6890]) *0 #TODO: implement this correctly
#SMPL-X Joint order: https://docs.google.com/spreadsheets/d/1_1dLdaX-sbMkCKr_JzJW_RZCpwBwd7rcKkWT_VgAQ_0/edit#gid=0
smplx_to_smpl = list(range(0, 22)) + [28, 43] + list(range(
55, 76)) #28 left middle finger , 43: right middle finger 1
smpl_joints = smpl_output.joints[:,
smplx_to_smpl, :] #Convert SMPL-X to SMPL 127 ->45
joints = torch.cat(
[smpl_joints, extra_joints], dim=1
) #[N, 127, 3]->[N, 45, 3] + [N, 9, 3] #SMPL-X has more joints. should convert 45
joints = joints[:, self.joint_map, :]
# Hand joints
smplx_hand_to_panoptic = [
0, 13, 14, 15, 16, 1, 2, 3, 17, 4, 5, 6, 18, 10, 11, 12, 19, 7, 8,
9, 20
] #Wrist Thumb to Pinky
smplx_lhand = [20] + list(range(25, 40)) + list(range(
66, 71)) #20 for left wrist. 20 finger joints
lhand_joints = smpl_output.joints[:, smplx_lhand, :] #(N,21,3)
lhand_joints = lhand_joints[:,
smplx_hand_to_panoptic, :] #Convert SMPL-X hand order to paonptic hand order
smplx_rhand = [21] + list(range(40, 55)) + list(range(
71, 76)) #21 for right wrist. 20 finger joints
rhand_joints = smpl_output.joints[:, smplx_rhand, :] #(N,21,3)
rhand_joints = rhand_joints[:,
smplx_hand_to_panoptic, :] #Convert SMPL-X hand order to paonptic hand order
output = ModelOutput(
vertices=smpl_output.vertices,
global_orient=smpl_output.global_orient,
body_pose=smpl_output.body_pose,
joints=joints,
right_hand_joints=rhand_joints, #N,21,3
left_hand_joints=lhand_joints, #N,21,3
betas=smpl_output.betas,
full_pose=smpl_output.full_pose)
return output
def forward(self, *args, **kwargs):
kwargs['get_skin'] = True
smpl_output = super(SMPL, self).forward(*args, **kwargs)
joints = vertices2joints(self.J_regressor_cocoplus,
smpl_output.vertices)[:, :14]
output = ModelOutput(vertices=smpl_output.vertices,
global_orient=smpl_output.global_orient,
body_pose=smpl_output.body_pose,
joints=joints,
betas=smpl_output.betas,
full_pose=smpl_output.full_pose)
return output
def forward(self, *args, **kwargs):
kwargs['get_skin'] = True
smpl_output = super(SMPL, self).forward(*args, **kwargs)
extra_joints = vertices2joints(self.J_regressor_extra, smpl_output.vertices)
joints = torch.cat([smpl_output.joints, extra_joints], dim=1)
joints = joints[:, self.joint_map, :]
output = ModelOutput(vertices=smpl_output.vertices, # 定点
global_orient=smpl_output.global_orient, #
body_pose=smpl_output.body_pose, # pose
joints=joints, # 关节
betas=smpl_output.betas, # shape
full_pose=smpl_output.full_pose) #
return output
def forward(self, *args, **kwargs):
kwargs['get_skin'] = True
smpl_output = super(SMPL, self).forward(*args, **kwargs)
extra_joints = vertices2joints(self.J_regressor_extra, smpl_output.vertices) #Additional 9 joints #Check doc/J_regressor_extra.png
joints = torch.cat([smpl_output.joints, extra_joints], dim=1) #[N, 24 + 21, 3] + [N, 9, 3]
joints = joints[:, self.joint_map, :]
output = ModelOutput(vertices=smpl_output.vertices,
global_orient=smpl_output.global_orient,
body_pose=smpl_output.body_pose,
joints=joints,
betas=smpl_output.betas,
full_pose=smpl_output.full_pose)
return output
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_all_from_pose(self,
poses,
regress=True,
th_betas=torch.zeros(1),
th_trans=torch.zeros(1)):
verts, Jtr = self.get_vert_from_pose(poses,
th_betas=th_betas,
th_trans=th_trans)
Jtr = self.vertex_joint_selector(verts, Jtr)
extra_joints = vertices2joints(self.J_regressor_extra,
verts).to(self.device)
joints = torch.cat([Jtr, extra_joints], dim=1).to(self.device)
# print(joints.shape, self.joint_map, extra_joints.shape)
joints = joints[:, self.joint_map, :]
if regress:
J_regressor_batch = self.J_regressor[None, :].expand(
verts.shape[0], -1, -1).to(self.device)
joints_36m = torch.matmul(J_regressor_batch, verts)
# Joints has 49 joints, Joints_36m has 17 joints
return verts.to(self.device), joints.to(self.device), joints_36m.to(
self.device)
