def optimize_pose_shape(th_scan_meshes,
smpl,
iterations,
steps_per_iter,
th_pose_3d=None,
display=None):
"""
Optimize SMPL.
:param display: if not None, pass index of the scan in th_scan_meshes to visualize.
"""
# Optimizer
optimizer = torch.optim.Adam([smpl.trans, smpl.betas, smpl.pose],
0.02,
betas=(0.9, 0.999))
# Get loss_weights
weight_dict = get_loss_weights()
# Display
if display is not None:
assert int(display) < len(th_scan_meshes)
mv = MeshViewer()
for it in range(iterations):
loop = tqdm(range(steps_per_iter))
loop.set_description('Optimizing SMPL')
for i in loop:
optimizer.zero_grad()
# Get losses for a forward pass
loss_dict = forward_step(th_scan_meshes, smpl, th_pose_3d)
# Get total loss for backward pass
tot_loss = backward_step(loss_dict, weight_dict, it)
tot_loss.backward()
optimizer.step()
l_str = 'Iter: {}'.format(i)
for k in loss_dict:
l_str += ', {}: {:0.4f}'.format(
k, weight_dict[k](loss_dict[k], it).mean().item())
loop.set_description(l_str)
if display is not None:
verts, _, _, _ = smpl()
smpl_mesh = Mesh(v=verts[display].cpu().detach().numpy(),
f=smpl.faces.cpu().numpy())
scan_mesh = Mesh(
v=th_scan_meshes[display].vertices.cpu().detach().numpy(),
f=th_scan_meshes[display].faces.cpu().numpy(),
vc=np.array([0, 1, 0]))
mv.set_static_meshes([scan_mesh, smpl_mesh])
print('** Optimised smpl pose and shape **')
def optimize_pose_shape(th_scan_meshes,
smplx,
iterations,
steps_per_iter,
scan_part_labels,
smplx_part_labels,
search_tree=None,
pen_distance=None,
tri_filtering_module=None,
display=None):
"""
Optimize SMPLX.
:param display: if not None, pass index of the scan in th_scan_meshes to visualize.
"""
# smplx.expression.requires_grad = False
# smplx.jaw_pose.requires_grad = False
# Optimizer
optimizer = torch.optim.Adam([
smplx.trans, smplx.betas, smplx.global_pose, smplx.body_pose,
smplx.left_hand_pose, smplx.right_hand_pose
],
0.02,
betas=(0.9, 0.999))
# Get loss_weights
weight_dict = get_loss_weights()
# Display
if display is not None:
assert int(display) < len(th_scan_meshes)
mv = MeshViewer()
for it in range(iterations):
loop = tqdm(range(steps_per_iter))
loop.set_description('Optimizing SMPLX')
for i in loop:
optimizer.zero_grad()
# Get losses for a forward pass
loss_dict = forward_step(th_scan_meshes, smplx, scan_part_labels,
smplx_part_labels, search_tree,
pen_distance, tri_filtering_module)
# Get total loss for backward pass
tot_loss = backward_step(loss_dict, weight_dict, it)
tot_loss.backward()
optimizer.step()
l_str = 'Iter: {}'.format(i)
for k in loss_dict:
l_str += ', {}: {:0.4f}'.format(
k, weight_dict[k](loss_dict[k], it).mean().item())
loop.set_description(l_str)
if display is not None:
# verts, _, _, _ = smplx()
verts = smplx()
smplx_mesh = Mesh(v=verts[display].cpu().detach().numpy(),
f=smplx.faces.cpu().numpy())
scan_mesh = Mesh(
v=th_scan_meshes[display].vertices.cpu().detach().numpy(),
f=th_scan_meshes[display].faces.cpu().numpy(),
vc=np.array([0, 1, 0]))
scan_mesh.set_vertex_colors_from_weights(
scan_part_labels[display].cpu().detach().numpy())
mv.set_static_meshes([scan_mesh, smplx_mesh])
print('** Optimised smplx pose and shape **')