add boundary optimization

data/registration_weights.py

@@ -0,0 +1,665 @@
+import os
+import sys
+import random
+import numpy as np
+import plotly.graph_objects as go
+import torch
+from torch.nn.functional import mse_loss
+from pytorch3d.structures import Meshes
+from pytorch3d.loss import mesh_laplacian_smoothing, mesh_edge_loss, mesh_normal_consistency, chamfer_distance
+from pymeshfix import MeshFix
+import scipy.spatial as spatial
+
+# os.environ["CUDA_LAUNCH_BLOCKING"] = '1'
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+from data.cloth3d_io import (
+    read_garment_mask,
+    read_garment_template,
+    read_posed_garment_frame,
+    loadInfo,
+    read_posed_body_frame,
+    read_posed_body,
+    read_posed_garment,
+    read_curvatures,
+    read_curvature_frame,
+    writeOBJ,
+    readOBJ,
+    quads2tris
+)
+from smpl.smpl_torch import SMPL
+# from custom_utils.custom_utils import _maskFaces
+
+device = 'cuda'
+smpl = SMPL(device, 'smpl')
+
+
+import torch.nn.functional as F
+from pytorch3d.structures import Meshes
+
+def find_closest_vertices(mesh1: Meshes, mesh2: Meshes, vertex_indices: torch.Tensor) -> torch.Tensor:
+    """
+    Given two PyTorch3D meshes and a set of vertex indices for mesh1,
+    find the closest vertices from mesh2 to these vertices.
+
+    Args:
+        mesh1: A PyTorch3D Meshes object representing the first mesh.
+        mesh2: A PyTorch3D Meshes object representing the second mesh.
+        vertex_indices: A tensor of shape (N,) representing the indices of the vertices in mesh1.
+
+    Returns:
+        A tensor of shape (N,) representing the indices of the closest vertices in mesh2.
+    """
+    # Extract the vertices of mesh1 and mesh2.
+    vertices1 = mesh1.verts_packed()
+    vertices2 = mesh2.verts_packed()
+
+    # Extract the vertices of mesh1 that correspond to the given indices.
+    vertices1_selected = vertices1[vertex_indices]
+
+    # Compute the pairwise distances between the selected vertices of mesh1 and all vertices of mesh2.
+    distances = F.pairwise_distance(vertices1_selected.unsqueeze(1), vertices2.unsqueeze(0), p=2)
+
+    # Find the indices of the closest vertices in mesh2.
+    closest_indices = torch.argmin(distances, dim=1)
+
+    return closest_indices
+
+
+def _maskFaces(V, F, mask, return_unfixed=False):
+    V_mask = mask
+    F_mask = np.take(V_mask, F.reshape(-1), axis=0)
+    F_mask = F_mask.reshape((-1, 3)).sum(axis=1) == 3
+
+    V_ind = np.zeros(V_mask.shape)
+    V_ind[V_mask] = np.arange(V_mask.sum())
+    F_new = np.take(V_ind, F[F_mask, :].reshape(-1), axis=0).reshape((-1, 3))
+    V_new = V[V_mask, :]
+
+    if return_unfixed:
+        return V_new, F_new, F[F_mask, :]
+    else:
+        return V_new, F_new
+
+def get_frames(folder, frames_file):
+    frames = []
+    with open(os.path.join(folder, frames_file), 'r') as f:
+        for line in f:
+            frames.append(line.strip())
+    return frames
+
+
+def plot(mesh, faces):
+    fig = go.Figure()
+
+    fig.add_trace(go.Mesh3d(
+                x=mesh[:,0],
+                y=mesh[:,1],
+                z=mesh[:,2],
+                i = faces[:,0],
+                j = faces[:,1],
+                k = faces[:,2]
+            ),)
+    
+    fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
+                    )
+    helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
+                yaxis = dict(nticks=4, range=[-1,1],),
+                zaxis = dict(nticks=4, range=[-1,1],),)
+    fig.update_layout(scene = helper,
+                        )
+
+    fig.show()
+
+
+def build_lookup_table(repeated):
+    # iterate through the list and create a dict from each sub array in the list where each dict has one element of the sub array as key and the other elements as value as a list and repeat for each element
+
+    lookup = {}
+    for i in range(len(repeated)):
+        for j in range(len(repeated[i])):
+            if len(repeated[i]) == 1:
+                continue
+            lookup[repeated[i][j]] = [repeated[i][k] for k in range(len(repeated[i]))] # if k != j
+    return lookup
+
+
+from data.cloth3d_io import readPC2Frame
+import scipy.io as sio
+import scipy
+import scipy.spatial as spatial
+
+def local_read_posed_garment_frame(folder, id, garment, frame, pose=None, deorient=False):
+    pc16_path = os.path.join(folder, id, garment + '.pc16')
+    verts = readPC2Frame(pc16_path, frame, True)
+
+    map = sio.loadmat(os.path.join(folder, id, garment + '_map.mat'))['map'].reshape((-1))
+
+    if deorient:
+        R = spatial.transform.Rotation.from_euler('xyz', pose[:3]).inv()
+        # R = spatial.transform.Rotation.from_euler([0,0,-zrot])
+        verts = R.apply(verts)
+
+    return verts, map
+
+def local_read_posed_body_frame(folder, id, frame, pose=None, deorient=False):
+    pc16_path = os.path.join(folder, id, 'Body_posed.pc16')
+    verts = readPC2Frame(pc16_path, frame, True)
+
+    if deorient:
+        R = spatial.transform.Rotation.from_euler('xyz', pose[:3]).inv()
+        # R = spatial.transform.Rotation.from_euler([0,0,-zrot])
+        verts = R.apply(verts)
+
+    return verts
+
+def edge_loss_custom(x, y, edges):
+    x_e = x[:,edges[:,0],:] - x[:,edges[:,1],:]
+    x_e = torch.sum(x_e ** 2, -1)
+    x_e = torch.sqrt(x_e + 1e-9)
+    y_e = y[:,edges[:,0],:] - y[:,edges[:,1],:]
+    y_e = torch.sum(y_e ** 2, -1)
+    y_e = torch.sqrt(y_e + 1e-9)
+    d_e = y_e - x_e
+    loss = torch.mean(d_e ** 2, dim=-1)[0]
+    return loss
+
+
+def crotch(mesh, body, crotch_idx, nr=75):
+    crotch_middle_on_body = crotch_idx
+    # create kdtree for body and mesh
+    body_tree = spatial.cKDTree(body)
+    mesh_tree = spatial.cKDTree(mesh)
+    # find closest point on mesh from crotch middle on body along the z axis
+
+    crotch_middle_on_mesh = find_closest_point_along_z(body[crotch_middle_on_body], mesh)
+
+    crotch_body = body_tree.query(body[crotch_middle_on_body], k=nr)[1]
+    crotch_mesh = mesh_tree.query(mesh[crotch_middle_on_mesh], k=nr)[1]
+
+    body_verts = body[crotch_body]
+    mesh_verts = mesh[crotch_mesh] - (mesh[crotch_middle_on_mesh] - body[crotch_middle_on_body])
+    mesh_verts = spatial.cKDTree(mesh_verts)
+    closest_mesh_verts = mesh_verts.query(body_verts, k=1)[1]
+    crotch_mesh = crotch_mesh[closest_mesh_verts]
+
+    return crotch_body, crotch_mesh
+
+
+def find_closest_point_along_z(point, verts, eps=0.01):
+    min_dist = np.inf
+    closest_point = None
+    for i, v in enumerate(verts):
+        dist = np.linalg.norm(point - v)
+        if dist < min_dist and np.abs(point[0] - v[0]) < eps and np.abs(point[1] - v[1]) < eps:
+            min_dist = dist
+            closest_point = i
+    return closest_point
+
+
+def mesh_edge_lengths(mesh: Meshes) -> torch.Tensor:
+    """
+    Given a PyTorch3D Meshes object, compute the lengths of all edges in the mesh.
+
+    Args:
+        mesh: A PyTorch3D Meshes object representing the mesh.
+
+    Returns:
+        A tensor of shape (E,) representing the lengths of all edges in the mesh,
+        where E is the total number of edges in the mesh.
+    """
+    # Extract the vertices and faces of the mesh.
+    vertices = mesh.verts_packed()
+    faces = mesh.faces_packed()
+
+    # Compute the pairwise distances between all pairs of vertices that form an edge.
+    edge_indices = faces.view(-1, 3)
+    edge_vertices = vertices[edge_indices]
+    edge_lengths = torch.norm(edge_vertices[:, 1:] - edge_vertices[:, :-1], dim=2)
+
+    return edge_lengths
+
+# try to optimize the already registered garment ??????????
+
+def register(folder, frame):
+    id, frame_nr, garment = frame.split()
+    frame_nr = int(frame_nr)
+    info = loadInfo(os.path.join(folder, id, 'info.mat'))
+    raw_mask = read_garment_mask(folder, id, garment, return_raw=True)
+    # convert it to indices
+    mask = raw_mask > 0
+
+    _, faces, _, _ = readOBJ(os.path.join(folder, id, garment + '.obj'))
+    faces = quads2tris(faces)
+    template = np.load('temp/mapping_cloth.npy')
+    mapping_faces = np.load('temp/mapping_faces.npy')
+    mapping_weights = np.load('temp/mapping_weights.npy')
+    mapping_mask = np.load('temp/mapping_mask.npy')
+
+    mapping_faces, mapping_weights = mapping_faces[mapping_mask], mapping_weights[mapping_mask]
+
+    is_skirt = (garment == 'Dress' or garment == 'Skirt')
+    skirt_mod = '_skirt' if is_skirt else ''
+    F = np.load(f'smpl/smpl_faces{skirt_mod}.npy').astype(np.int32)
+    _, registered_faces = _maskFaces(np.zeros((14475, 3)), F, mapping_mask)
+
+
+    template = torch.tensor(template).to(device).to(torch.float32)
+    template_orig = template.clone()
+    faces = torch.tensor(faces).to(device)
+    mapping_faces = torch.tensor(mapping_faces).to(device)
+    mapping_weights = torch.tensor(mapping_weights).to(device).to(torch.float32)
+    F = torch.tensor(F).to(device)
+    registered_faces = torch.tensor(registered_faces).to(device)
+
+    # verts = template.cpu().numpy()
+    # star_body = star_body.cpu().numpy()
+    # faces = faces.detach().cpu().numpy()
+    # F = F.detach().cpu().numpy()
+    # fig = go.Figure(data=[
+    #     go.Mesh3d(
+    #         x=verts[:,0], y=verts[:,1], z=verts[:,2],
+    #         i=faces[:,0], j=faces[:,1], k=faces[:,2],
+    #         color='lightpink', opacity=0.50),
+    #     go.Mesh3d(
+    #         x=star_body[:,0], y=star_body[:,1], z=star_body[:,2],
+    #         i=F[:,0], j=F[:,1], k=F[:,2],
+    #         color='lightblue', opacity=0.50),
+    #     go.Scatter3d(
+    #         x=verts[crotch_mesh,0], y=verts[crotch_mesh,1], z=verts[crotch_mesh,2],
+    #         mode='markers', marker=dict(size=2, color='red')),
+    #     go.Scatter3d(
+    #         x=star_body[crotch_body,0], y=star_body[crotch_body,1], z=star_body[crotch_body,2],
+    #         mode='markers', marker=dict(size=2, color='blue')),
+    # ])
+    # # make it cuve and limit to -1 and -1
+    # fig.update_layout(scene = dict(
+    #     xaxis = dict(range=[-1,1],),
+    #     yaxis = dict(range=[-1,1],),
+    #     zaxis = dict(range=[-1,1],),),)
+    # fig.show()
+
+    mesh2 = Meshes(verts=template[None,:,:], faces=faces[None,:,:])
+    avg_edge_length = mesh_edge_lengths(mesh2).mean()
+
+    # Define the meshes to optimize
+    registered = (template[mapping_faces,:] * mapping_weights[:,:,None]).sum(1)
+
+    optim_mask = torch.ones(registered.shape[0]).to(device).to(torch.bool)
+    # registered_tree = spatial.KDTree(template.detach().cpu().numpy())
+    # for i in range(registered.shape[0]):
+    #     # if distance between registerd and template is too big set it to require grad that weight
+    #     res = registered_tree.query(registered[i].detach().cpu().numpy(), k=1)
+    #     if res[0] > avg_edge_length / 4:
+    #         optim_mask[i] = True
+
+    mapping_weights_masked = mapping_weights[optim_mask]
+    mapping_weights_nonmasked = mapping_weights[~optim_mask]
+    mapping_weights_masked.requires_grad = True
+
+    # mesh1 = Meshes(verts=registered[None,:,:], faces=registered_faces[None,:,:])
+
+
+    # Define the optimizer and learning rate
+    optimizer = torch.optim.Adam([mapping_weights_masked], lr=0.001)
+    # optimizer = torch.optim.SGD([mesh1.verts_packed()], lr=0.001)
+
+    # Define the loss functions
+    def laplacian_smoothing_loss(mesh):
+        return mesh_laplacian_smoothing(mesh, method="uniform") # cotcurv uniform cot
+
+    def edge_loss(mesh):
+        return mesh_edge_loss(mesh, target_length=avg_edge_length)
+
+    def normal_consistency_loss(mesh):
+        return mesh_normal_consistency(mesh)
+
+    # Optimization loop
+    for i in range(4000):
+        optimizer.zero_grad()
+
+        # find boundaries with the find holes function?
+        registered_masked = (template[mapping_faces[optim_mask],:] * mapping_weights_masked[:,:,None]).sum(1)
+        registered_nonmasked = (template[mapping_faces[~optim_mask],:] * mapping_weights_nonmasked[:,:,None]).sum(1)
+        # registered = torch.cat((registered_masked, registered_nonmasked), 0)
+        registered = torch.zeros_like(mapping_weights)
+        registered[optim_mask] = registered_masked
+        registered[~optim_mask] = registered_nonmasked
+
+        # Forward pass
+        mesh1_out = Meshes(verts=registered[None,:,:], faces=registered_faces[None,:,:])
+        ch_loss, _ = chamfer_distance(torch.stack(mesh1_out.verts_list()), torch.stack(mesh2.verts_list()),
+                                        )
+        loss: torch.Tensor = ch_loss * 20 #+ ch_norm_loss * 10
+        lap_loss = laplacian_smoothing_loss(mesh1_out)
+        e_loss = edge_loss(mesh1_out)
+        # loss += edge_loss_custom(torch.stack(mesh1_out.verts_list()), template_orig[None,:,:], mesh1.edges_packed()) * 100
+        norm_loss = normal_consistency_loss(mesh1_out)
+        loss += lap_loss * 0.05 + e_loss * 20 + norm_loss * 0.1
+
+        # Backward pass
+        loss.backward()
+        optimizer.step()
+
+        # Print the loss
+        if i % 10 == 0:
+            print(f"it: {i} : loss: {loss:.5f} chamfer: {ch_loss:.6f} laplacian: {lap_loss:.6f} edge: {e_loss:.6f} normal: {norm_loss:.5f}")
+    
+    # create a plotly plot with the body and the mesh1_out
+    registered = mesh1_out.verts_packed().detach().cpu().numpy()
+    template = mesh2.verts_packed().detach().cpu().numpy()
+    faces = faces.detach().cpu().numpy()
+    F = F.detach().cpu().numpy()
+    optim_mask = optim_mask.detach().cpu().numpy()
+    registered_faces = registered_faces.detach().cpu().numpy()
+    # faces, F = F, faces
+    # verts, star_body = star_body, verts
+
+    fig = go.Figure(data=[
+        go.Mesh3d(
+            x=template[:,0], y=template[:,1], z=template[:,2],
+            i=faces[:,0], j=faces[:,1], k=faces[:,2],
+            color='lightblue', opacity=0.80),
+        go.Mesh3d(
+            x=registered[:,0], y=registered[:,1], z=registered[:,2],
+            i=registered_faces[:,0], j=registered_faces[:,1], k=registered_faces[:,2],
+            color='lightpink', opacity=0.50),   
+        # go.Scatter3d(
+        #     x=template[:,0], y=template[:,1], z=template[:,2],
+        #     mode='markers', marker=dict(size=2, color='red')),
+        # go.Scatter3d(
+        #     x=registered[:,0], y=registered[:,1], z=registered[:,2],
+        #     mode='markers', marker=dict(size=2, color='blue')),
+    ])
+    # make it cuve and limit to -1 and -1
+    fig.update_layout(scene = dict(
+        xaxis = dict(range=[-1,1],),
+        yaxis = dict(range=[-1,1],),
+        zaxis = dict(range=[-1,1],),),)
+    fig.update_layout(scene_aspectmode='cube')
+    fig.show()
+
+
+    fig = go.Figure(data=[
+        go.Scatter3d(
+            x=registered[:,0], y=registered[:,1], z=registered[:,2],
+            mode='markers', marker=dict(size=4, color='red')),
+        go.Scatter3d(
+            x=template[:,0], y=template[:,1], z=template[:,2],
+            mode='markers', marker=dict(size=2, color='blue')),
+    ])
+    # make it cuve and limit to -1 and -1
+    fig.update_layout(scene = dict(
+        xaxis = dict(range=[-1,1],),
+        yaxis = dict(range=[-1,1],),
+        zaxis = dict(range=[-1,1],),),)
+    fig.update_layout(scene_aspectmode='cube')
+    fig.show()
+
+    
+    # fig = go.Figure(data=[
+    #     go.Scatter3d(
+    #         x=registered[:optim_mask.sum(),0], y=registered[:optim_mask.sum(),1], z=registered[:optim_mask.sum(),2],
+    #         mode='markers', marker=dict(size=4, color='red')),
+    #     go.Scatter3d(
+    #         x=registered[:,0], y=registered[:,1], z=registered[:,2],
+    #         mode='markers', marker=dict(size=2, color='blue')),
+    # ])
+    # # make it cuve and limit to -1 and -1
+    # fig.update_layout(scene = dict(
+    #     xaxis = dict(range=[-1,1],),
+    #     yaxis = dict(range=[-1,1],),
+    #     zaxis = dict(range=[-1,1],),),)
+    # fig.update_layout(scene_aspectmode='cube')
+    # fig.show()
+
+    # create a plotly wireframe plot of registered
+    # fig = go.Figure()
+    # for edge in mesh1_out.edges_packed().detach().cpu().numpy():
+    #     fig.add_trace(go.Scatter3d(
+    #         x=registered[edge,0], y=registered[edge,1], z=registered[edge,2],
+    #         mode='lines', line=dict(color='red', width=1)))
+    # fig.update_layout(scene = dict(
+    #     xaxis = dict(range=[-1,1],),
+    #     yaxis = dict(range=[-1,1],),
+    #     zaxis = dict(range=[-1,1],),),)
+    # fig.update_layout(scene_aspectmode='cube')
+    # fig.show()
+
+
+    pass
+
+
+def get_boundaries(folder, frame):
+    from pymeshfix import MeshFix
+    from scipy.spatial import KDTree
+
+    id, frame_nr, garment = frame.split()
+    frame_nr = int(frame_nr)
+    registered_cloth = np.load('temp/registered_cloth.npy')
+    registered_faces = np.load('temp/registered_faces.npy')
+    registred_weights = np.load('temp/registered_weights.npy')
+    _, faces, _, _ = readOBJ(os.path.join(folder, id, garment + '.obj'))
+    faces = quads2tris(faces)
+    template = np.load('temp/mapping_cloth.npy')
+
+    registered_meshfix = MeshFix(registered_cloth, registered_faces)
+    registered_holes = registered_meshfix.extract_holes()
+
+    template_meshfix = MeshFix(template, faces)
+    template_holes = template_meshfix.extract_holes()
+    template_border = []
+    for border in np.array(template_holes.points):
+        # find the index of the vertex in the original mesh
+        ind = np.argwhere(np.all(template == border, axis=1))
+        template_border.extend(ind.ravel())
+
+    template_border_tree = KDTree(template[template_border])
+
+    registered_border = []
+    for border in np.array(registered_holes.points):
+        q = template_border_tree.query(border)
+        if q[0] > 0.02:
+            continue
+        # find the index of the vertex in the original mesh
+        ind = np.argwhere(np.all(registered_cloth == border, axis=1))
+        registered_border.extend(ind.ravel())
+
+    np.save('temp/registered_border.npy', registered_border)
+    np.save('temp/template_border.npy', template_border)
+    
+    # plot registered cloth and tempalte as scatterplots and add another two scatter plots for the two boundary sets
+    fig = go.Figure(data=[
+        go.Scatter3d(
+            x=registered_cloth[:,0], y=registered_cloth[:,1], z=registered_cloth[:,2],
+            mode='markers', marker=dict(size=2, color='red')),
+        go.Scatter3d(
+            x=registered_cloth[registered_border,0], y=registered_cloth[registered_border,1], z=registered_cloth[registered_border,2],
+            mode='markers', marker=dict(size=4, color='green')),
+    ])
+    fig.update_layout(scene = dict(
+        xaxis = dict(range=[-1,1],),
+        yaxis = dict(range=[-1,1],),
+        zaxis = dict(range=[-1,1],),),)
+    fig.update_layout(scene_aspectmode='cube')
+    fig.show()
+
+    fig = go.Figure(data=[
+        go.Scatter3d(
+            x=template[:,0], y=template[:,1], z=template[:,2],
+            mode='markers', marker=dict(size=2, color='blue')),
+        go.Scatter3d(
+            x=template[template_border,0], y=template[template_border,1], z=template[template_border,2],
+            mode='markers', marker=dict(size=4, color='green')),
+    ])
+    fig.update_layout(scene = dict(
+        xaxis = dict(range=[-1,1],),
+        yaxis = dict(range=[-1,1],),
+        zaxis = dict(range=[-1,1],),),)
+    fig.update_layout(scene_aspectmode='cube')
+    fig.show()
+
+    pass
+
+def optimize_boundaries(folder, frame):
+    id, frame_nr, garment = frame.split()
+    frame_nr = int(frame_nr)
+    registered_cloth = np.load('temp/registered_cloth.npy')
+    registered_faces = np.load('temp/registered_faces.npy')
+    registered_weights = np.load('temp/registered_weights.npy')
+    _, faces, _, _ = readOBJ(os.path.join(folder, id, garment + '.obj'))
+    faces = quads2tris(faces)
+    template = np.load('temp/mapping_cloth.npy')
+    mapping_mask = np.load('temp/mapping_mask.npy')
+    mapping_faces = np.load('temp/mapping_faces.npy')
+    mapping_weights = np.load('temp/mapping_weights.npy')
+
+    mapping_faces, mapping_weights = mapping_faces[mapping_mask], mapping_weights[mapping_mask]
+
+    template_border = np.load('temp/template_border.npy')
+    registered_border = np.load('temp/registered_border.npy')
+
+
+    template = torch.tensor(template).to(device).to(torch.float32)
+    faces = torch.tensor(faces).to(device)
+    registered_faces = torch.tensor(registered_faces).to(device)
+    mapping_faces = torch.tensor(mapping_faces).to(device)
+    registered_weights = torch.tensor(registered_weights).to(device).to(torch.float32)
+    registered_cloth = torch.tensor(registered_cloth).to(device).to(torch.float32)
+
+
+    mesh2 = Meshes(verts=template[None,:,:], faces=faces[None,:,:])
+    avg_edge_length = mesh_edge_lengths(mesh2).mean()
+
+    optim_mask = torch.zeros(registered_cloth.shape[0]).to(device).to(torch.bool)
+    optim_mask[registered_border] = True
+
+    mapping_weights_masked = registered_weights[optim_mask]
+    mapping_weights_nonmasked = registered_weights[~optim_mask]
+    mapping_weights_masked.requires_grad = True
+
+    # Define the optimizer and learning rate
+    optimizer = torch.optim.Adam([mapping_weights_masked], lr=0.001)
+    # optimizer = torch.optim.SGD([mesh1.verts_packed()], lr=0.001)
+
+    # Define the loss functions
+    def laplacian_smoothing_loss(mesh):
+        return mesh_laplacian_smoothing(mesh, method="uniform") # cotcurv uniform cot
+
+    def edge_loss(mesh):
+        return mesh_edge_loss(mesh, target_length=avg_edge_length)
+
+    def normal_consistency_loss(mesh):
+        return mesh_normal_consistency(mesh)
+
+    # Optimization loop
+    for i in range(4000):
+        optimizer.zero_grad()
+
+        # find boundaries with the find holes function?
+        registered_masked = (template[mapping_faces[optim_mask],:] * mapping_weights_masked[:,:,None]).sum(1)
+        registered_nonmasked = (template[mapping_faces[~optim_mask],:] * mapping_weights_nonmasked[:,:,None]).sum(1)
+        # registered = torch.cat((registered_masked, registered_nonmasked), 0)
+        registered = torch.zeros_like(registered_weights)
+        registered[optim_mask] = registered_masked
+        registered[~optim_mask] = registered_nonmasked
+
+        # Forward pass
+        mesh1_out = Meshes(verts=registered[None,:,:], faces=registered_faces[None,:,:])
+        # ch_loss, _ = chamfer_distance(torch.stack(mesh1_out.verts_list()), torch.stack(mesh2.verts_list()),
+        #                                 )
+        ch_bound_loss, _ = chamfer_distance(torch.stack(mesh1_out.verts_list())[:,registered_border,:], torch.stack(mesh2.verts_list())[:, template_border,:],
+                                        )
+        # loss: torch.Tensor = ch_loss * 1 + ch_bound_loss * 20
+        loss: torch.Tensor = ch_bound_loss * 50
+        lap_loss = laplacian_smoothing_loss(mesh1_out)
+        e_loss = edge_loss(mesh1_out)
+        # loss += edge_loss_custom(torch.stack(mesh1_out.verts_list()), template_orig[None,:,:], mesh1.edges_packed()) * 100
+        norm_loss = normal_consistency_loss(mesh1_out)
+        loss += lap_loss * 0.05 + e_loss * 2 + norm_loss * 0.1
+
+        # Backward pass
+        loss.backward()
+        optimizer.step()
+
+        # Print the loss
+        if i % 10 == 0:
+            print(f"it: {i} : loss: {loss:.5f} bound_chamfer: {ch_bound_loss:.6f}")
+            # print(f"it: {i} : loss: {loss:.5f} bound_chamfer: {ch_bound_loss:.6f} chamfer: {ch_loss:.6f} laplacian: {lap_loss:.6f} edge: {e_loss:.6f} normal: {norm_loss:.5f}")
+    
+    registered = mesh1_out.verts_packed().detach().cpu().numpy()
+    template = mesh2.verts_packed().detach().cpu().numpy()
+    faces = faces.detach().cpu().numpy()
+    optim_mask = optim_mask.detach().cpu().numpy()
+    registered_faces = registered_faces.detach().cpu().numpy()
+    # faces, F = F, faces
+    # verts, star_body = star_body, verts
+
+    fig = go.Figure(data=[
+        go.Mesh3d(
+            x=template[:,0], y=template[:,1], z=template[:,2],
+            i=faces[:,0], j=faces[:,1], k=faces[:,2],
+            color='lightblue', opacity=0.80),
+        go.Mesh3d(
+            x=registered[:,0], y=registered[:,1], z=registered[:,2],
+            i=registered_faces[:,0], j=registered_faces[:,1], k=registered_faces[:,2],
+            color='lightpink', opacity=0.50),   
+
+    ])
+    fig.update_layout(scene = dict(
+        xaxis = dict(range=[-1,1],),
+        yaxis = dict(range=[-1,1],),
+        zaxis = dict(range=[-1,1],),),)
+    fig.update_layout(scene_aspectmode='cube')
+    fig.show()
+
+
+    fig = go.Figure(data=[
+        go.Scatter3d(
+            x=registered[:,0], y=registered[:,1], z=registered[:,2],
+            mode='markers', marker=dict(size=4, color='red')),
+        go.Scatter3d(
+            x=template[:,0], y=template[:,1], z=template[:,2],
+            mode='markers', marker=dict(size=2, color='blue')),
+    ])
+    fig.update_layout(scene = dict(
+        xaxis = dict(range=[-1,1],),
+        yaxis = dict(range=[-1,1],),
+        zaxis = dict(range=[-1,1],),),)
+    fig.update_layout(scene_aspectmode='cube')
+    fig.show()
+
+    pass
+
+if __name__ == "__main__":
+    folder = '/data-net/code/cloth3d/train'
+    # folder = r'D:\Work\code\cloth3d\train'
+    # frames = [f'00761_{i}' for i in range(300)]
+    # folder = r'Z:\Work\Huni\Barcelona\Dataset\cloth3d\train'
+    frames = get_frames(folder, 'frames_all.txt')
+    root = '/'.join(folder.split('/')[:-1])
+    type = folder.split('/')[-1]
+
+    use_no_orient = False
+    no_shape = False
+    is_skirt = False
+
+    skirt_modifier = '_skirt' if is_skirt else ''
+    orient_mod = '_no_orient' if use_no_orient else ''
+    shape_mod = '_no_shape' if no_shape else ''
+
+    # mask = np.load('/data-net/dataset/00761/mesh_mask/00761.npy')
+    # faces = np.load('/data-net/dataset/00761/faces/00761.npy')
+
+    for frame in frames:
+        frame = random.choice(frames)
+        # frame = '00761 0 Tshirt'
+        # frame = '01893 256 Jumpsuit'
+        frame = '05060 1 Jumpsuit'
+        
+
+        # register(folder, frame)
+        # get_boundaries(folder, frame)
+        optimize_boundaries(folder, frame)
+        break
+        pass