def closure():
camera_optimizer.zero_grad()
betas_ext = arrange_betas(body_pose, betas)
smpl_output = self.smpl(global_orient=global_orient,
body_pose=body_pose,
betas=betas_ext)
model_joints = smpl_output.joints
loss = temporal_camera_fitting_loss(model_joints, camera_translation,
init_cam_t, camera_center,
joints_2d, joints_conf, focal_length=self.focal_length)
loss.backward()
return loss
def __call__(self, init_pose, init_betas, init_cam_t, camera_center, keypoints_2d):
"""Perform body fitting.
Input:
init_pose: SMPL pose estimate
init_betas: SMPL betas estimate
init_cam_t: Camera translation estimate
camera_center: Camera center location
keypoints_2d: Keypoints used for the optimization
Returns:
vertices: Vertices of optimized shape
joints: 3D joints of optimized shape
pose: SMPL pose parameters of optimized shape
betas: SMPL beta parameters of optimized shape
camera_translation: Camera translation
reprojection_loss: Final joint reprojection loss
"""
# Make camera translation a learnable parameter
camera_translation = init_cam_t.clone()
# Get joint confidence
joints_2d = keypoints_2d[:, :, :2]
joints_conf = keypoints_2d[:, :, -1]
# Split SMPL pose to body pose and global orientation
body_pose = init_pose[:, 3:].detach().clone()
global_orient = init_pose[:, :3].detach().clone()
betas = init_betas.detach().clone()
# Step 1: Optimize camera translation and body orientation
# Optimize only camera translation and body orientation
body_pose.requires_grad = False
betas.requires_grad = False
global_orient.requires_grad = True
camera_translation.requires_grad = True
camera_opt_params = [global_orient, camera_translation]
if self.use_lbfgs:
camera_optimizer = torch.optim.LBFGS(camera_opt_params, max_iter=self.max_iter,
lr=self.step_size, line_search_fn='strong_wolfe')
for i in range(self.num_iters):
def closure():
camera_optimizer.zero_grad()
betas_ext = arrange_betas(body_pose, betas)
smpl_output = self.smpl(global_orient=global_orient,
body_pose=body_pose,
betas=betas_ext)
model_joints = smpl_output.joints
loss = temporal_camera_fitting_loss(model_joints, camera_translation,
init_cam_t, camera_center,
joints_2d, joints_conf, focal_length=self.focal_length)
loss.backward()
return loss
camera_optimizer.step(closure)
else:
camera_optimizer = torch.optim.Adam(camera_opt_params, lr=self.step_size, betas=(0.9, 0.999))
for i in range(self.num_iters):
betas_ext = arrange_betas(body_pose, betas)
smpl_output = self.smpl(global_orient=global_orient,
body_pose=body_pose,
betas=betas_ext)
model_joints = smpl_output.joints
loss = temporal_camera_fitting_loss(model_joints, camera_translation,
init_cam_t, camera_center,
joints_2d, joints_conf, focal_length=self.focal_length)
camera_optimizer.zero_grad()
loss.backward()
camera_optimizer.step()
# Fix camera translation after optimizing camera
camera_translation.requires_grad = False
# Step 2: Optimize body joints
# Optimize only the body pose and global orientation of the body
body_pose.requires_grad = True
betas.requires_grad = True
global_orient.requires_grad = True
camera_translation.requires_grad = False
body_opt_params = [body_pose, betas, global_orient]
# For joints ignored during fitting, set the confidence to 0
joints_conf[:, self.ign_joints] = 0.
if self.use_lbfgs:
body_optimizer = torch.optim.LBFGS(body_opt_params, max_iter=self.max_iter,
lr=self.step_size, line_search_fn='strong_wolfe')
for i in range(self.num_iters):
def closure():
body_optimizer.zero_grad()
betas_ext = arrange_betas(body_pose, betas)
smpl_output = self.smpl(global_orient=global_orient,
body_pose=body_pose,
betas=betas_ext)
model_joints = smpl_output.joints
loss = temporal_body_fitting_loss(body_pose, betas, model_joints, camera_translation, camera_center,
joints_2d, joints_conf, self.pose_prior,
focal_length=self.focal_length)
loss.backward()
return loss
body_optimizer.step(closure)
else:
body_optimizer = torch.optim.Adam(body_opt_params, lr=self.step_size, betas=(0.9, 0.999))
for i in range(self.num_iters):
betas_ext = arrange_betas(body_pose, betas)
smpl_output = self.smpl(global_orient=global_orient,
body_pose=body_pose,
betas=betas_ext)
model_joints = smpl_output.joints
loss = temporal_body_fitting_loss(body_pose, betas, model_joints, camera_translation, camera_center,
joints_2d, joints_conf, self.pose_prior,
focal_length=self.focal_length)
body_optimizer.zero_grad()
loss.backward()
body_optimizer.step()
# scheduler.step(epoch=i)
# Get final loss value
with torch.no_grad():
betas_ext = arrange_betas(body_pose, betas)
smpl_output = self.smpl(global_orient=global_orient,
body_pose=body_pose,
betas=betas_ext, return_full_pose=True)
model_joints = smpl_output.joints
reprojection_loss = temporal_body_fitting_loss(body_pose, betas, model_joints, camera_translation,
camera_center,
joints_2d, joints_conf, self.pose_prior,
focal_length=self.focal_length,
output='reprojection')
vertices = smpl_output.vertices.detach()
joints = smpl_output.joints.detach()
pose = torch.cat([global_orient, body_pose], dim=-1).detach()
betas = betas.detach()
# Back to weak perspective camera
camera_translation = torch.stack([
2 * 5000. / (224 * camera_translation[:,2] + 1e-9),
camera_translation[:,0], camera_translation[:,1]
], dim=-1)
betas = betas.repeat(pose.shape[0],1)
output = {
'theta': torch.cat([camera_translation, pose, betas], dim=1),
'verts': vertices,
'kp_3d': joints,
}
return output, reprojection_loss