Upload 41 files

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+animation/inputs/crocodile/input.mp4 filter=lfs diff=lfs merge=lfs -text
+animation/inputs/fish/input.mp4 filter=lfs diff=lfs merge=lfs -text
+animation/inputs/fish/objs/fish_texture.png filter=lfs diff=lfs merge=lfs -text

animation/README.md

@@ -0,0 +1,77 @@
+# 3D Animation with Video Guidance
+This repository provides a complete pipeline for generating 3D object animations with video guidance. The system includes data processing and optimization algorithms for rigging-based animation.
+
+## Overview
+The pipeline takes a rigged 3D model and a reference video, then optimizes the object's motion to match the video guidance while maintaining realistic skeletal constraints.
+
+## Prerequisites
+
+### Model Downloads
+Download the required pre-trained models:
+
+- [Video-Depth-Anything](https://huggingface.co/depth-anything/Video-Depth-Anything-Large) - For depth estimation
+- [CoTracker3](https://huggingface.co/facebook/cotracker3) - For point tracking
+
+```
+python download.py
+```
+
+### Input Data Structure
+
+Organize your input data as follows:
+```
+inputs/
+└── {seq_name}/
+    ├── objs/
+    │   ├── mesh.obj          # 3D mesh geometry
+    │   ├── rig.txt           # Rigging definition
+    │   ├── material.mtl      # Material properties (optional)
+    │   └── texture.png       # Texture maps (optional)
+    ├── first_frames/         # Rendered initial frames
+    ├── imgs/                 # Extracted video frames
+    ├── flow/                 # Optical flow data
+    ├── flow_vis/             # Visualized optical flow
+    ├── depth/                # Esitmated depth data
+    ├── track_2d_verts/       # tracked vertices
+    ├── track_2d_joints/      # tracked joints
+    └── input.mp4             # Source video
+```
+
+## Data Processing
+
+Given a 3D model with rigging under `inputs/{seq_name}/objs` (`mesh.obj, rig.txt`, optional `.mtl` and texture `.png`), we first render the object from a specified viewpoint. This image is used as the input (first frame) to the video generation model (e.g., [Jimeng AI](https://jimeng.jianying.com/ai-tool/home?type=video)).
+
+```
+python utils/render_first_frame.py --input_path inputs --seq_name {seq_name}
+```
+Replace `{seq_name}` with your sequence name. The first-frame images are saved to `inputs/{seq_name}/first_frames`. This generates reference images from 4 different viewpoints (you can add more). Choose the viewpoint that best shows the object's joints and key parts for optimal animation results. Save the generated videos to `inputs/{seq_name}/input.mp4`.
+
+Then we extract the frames from the video by running:
+
+```
+cd inputs/{seq_name}; mkdir imgs
+ffmpeg -i input.mp4 -vf fps=10 imgs/frame_%04d.png
+cd ../../
+```
+
+Estimate optical flows by running:
+
+```
+python utils/save_flow.py --input_path inputs --seq_name {seq_name}
+```
+The flow `.flo` files are saved to `inputs/{seq_name}/flow`, the flow visualization are saved to `inputs/{seq_name}/flow_vis`. Depth and tracking information are saved during optimization.
+
+## Optimization
+
+To optimize the animation, you can run
+
+```
+bash demo.sh
+```
+
+The results are saved to `results/{seq_name}/{save_name}`. Modify `--main_renderer` and `--additional_renderers` to change rendering viewpoints. If animations exhibit jitter or instability, increase the root/joint smoothing weights for better temporal consistency.
+
+
+## TODO
+
+- [ ] Add multi-view supervisions.

animation/demo.sh

@@ -0,0 +1,7 @@
+python optimization.py --save_path results --iter 200 --input_path inputs --img_size 960 \
+        --seq_name 'fish' --save_name 'fish' --coherence_weight 5
+
+# python optimization.py --save_path results --iter 200 --input_path inputs --img_size 960 \
+#         --seq_name 'crocodile' --save_name 'crocodile_demo'  --coherence_weight 15
+
+

animation/download.py

@@ -0,0 +1,13 @@
+from huggingface_hub import hf_hub_download
+
+file_path = hf_hub_download(
+    repo_id="facebook/cotracker3",
+    filename="scaled_offline.pth",
+    local_dir="third_partys/co_tracker/ckpt"
+)
+
+file_path = hf_hub_download(
+    repo_id="depth-anything/Video-Depth-Anything-Large",
+    filename="video_depth_anything_vitl.pth",
+    local_dir="third_partys/Video_Depth_Anything/ckpt"
+)

animation/inputs/crocodile/input.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1182d9336eaf8946c2099100508f123cf70763af8053889f67fd227ce7c49b9b
+size 2584084

animation/inputs/crocodile/objs/material.mtl

@@ -0,0 +1,7 @@
+newmtl CrocodileMaterial_01
+Ka 1.000000 1.000000 1.000000
+Kd 0.800000 0.800000 0.800000
+Ks 0.000000 0.000000 0.000000
+Ns 10.000000
+illum 2
+

animation/inputs/crocodile/objs/rig.txt

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animation/inputs/fish/objs/material.mtl

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animation/inputs/fish/objs/rig.txt

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animation/model.py

@@ -0,0 +1,199 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import torch
+from typing import List, Optional, Tuple, Union
+from collections import deque
+from pytorch3d.structures import Meshes, join_meshes_as_scene
+from pytorch3d.renderer import TexturesVertex, TexturesUV
+
+from utils.quat_utils import quat_to_transform_matrix, quat_multiply, quat_rotate_vector
+
+class RiggingModel:
+    """
+    A 3D rigged model supporting skeletal animation.
+    
+    Handles mesh geometry, skeletal hierarchy, skinning weights, and 
+    linear blend skinning (LBS) deformation.
+    """
+    def __init__(self, device = "cuda:0"):
+        self.device = device
+        # Mesh data
+        self.vertices: List[torch.Tensor] = []    
+        self.faces: List[torch.Tensor] = []        
+        self.textures: List[Union[TexturesVertex, TexturesUV]] = [] 
+        
+        # Skeletal data
+        self.bones: Optional[torch.Tensor] = None       # (N, 2) [parent, child] pairs
+        self.parent_indices: Optional[torch.Tensor] = None  # (J,) parent index for each joint
+        self.root_index: Optional[int] = None           # Root joint index
+        self.joints_rest: Optional[torch.Tensor] = None # (J, 3) rest pose positions
+        self.skin_weights: List[torch.Tensor] = [] # List of (V_i, J) skinning weights
+
+        # Fixed local positions
+        self.rest_local_positions: Optional[torch.Tensor] = None  # (J, 3)
+        
+        # Computed data
+        self.bind_matrices_inv: Optional[torch.Tensor] = None  # (J, 4, 4) inverse bind matrices
+        self.deformed_vertices: Optional[List[torch.Tensor]] = None  # List of (T, V_i, 3)
+        self.joint_positions: Optional[torch.Tensor] = None    # (T, J, 3) current joint positions
+        
+        # Validation flags
+        self._bind_matrices_initialized = False
+
+    def initialize_bind_matrices(self, rest_local_pos):
+        """Initialize bind matrices and store rest local positions."""
+        self.rest_local_positions = rest_local_pos.to(self.device)
+        
+        J = rest_local_pos.shape[0]
+        rest_global_quats, rest_global_pos = self.forward_kinematics(
+            torch.tensor([[[1.0, 0.0, 0.0, 0.0]] * J], device=self.device),  # unit quaternion
+            self.parent_indices,
+            self.root_index
+        )
+        
+        bind_matrices = quat_to_transform_matrix(rest_global_quats, rest_global_pos)  # (1,J,4,4)
+        self.bind_matrices_inv = torch.inverse(bind_matrices.squeeze(0))  # (J,4,4)
+        
+        self._bind_matrices_initialized = True
+
+    def animate(self, local_quaternions, root_quaternion = None, root_position = None):
+        """
+        Animate the model using local joint transformations.
+        
+        Args:
+            local_quaternions: (T, J, 4) local rotations per frame
+            root_quaternion: (T, 4) global root rotation
+            root_position: (T, 3) global root translation
+        """
+        if not self._bind_matrices_initialized:
+            raise RuntimeError("Bind matrices not initialized. Call initialize_bind_matrices() first.")
+        
+        # Forward kinematics
+        global_quats, global_pos = self.forward_kinematics(
+            local_quaternions,
+            self.parent_indices,
+            self.root_index
+        )
+        self.joint_positions = global_pos
+
+        joint_transforms = quat_to_transform_matrix(global_quats, global_pos)  # (T, J, 4, 4)
+        
+        # Apply global root transformation if provided
+        if root_quaternion is not None and root_position is not None:
+            root_transform = quat_to_transform_matrix(root_quaternion, root_position)
+            joint_transforms = root_transform[:, None] @ joint_transforms
+            self.joint_positions = joint_transforms[..., :3, 3]
+
+        # Linear blend skinning
+        self.deformed_vertices = []
+        for i, vertices in enumerate(self.vertices):
+            deformed = self._linear_blend_skinning(
+                vertices,
+                joint_transforms,
+                self.skin_weights[i],
+                self.bind_matrices_inv
+            )
+            self.deformed_vertices.append(deformed)
+
+
+    def get_mesh(self, frame_idx=None):
+        meshes = []
+        for i in range(len(self.vertices)):
+            mesh = Meshes(
+                verts=[self.vertices[i]] if frame_idx is None or self.deformed_vertices is None else [self.deformed_vertices[i][frame_idx]],
+                faces=[self.faces[i]],
+                textures=self.textures[i]
+            )
+            meshes.append(mesh)
+        return join_meshes_as_scene(meshes)
+    
+    def _linear_blend_skinning(self, vertices, joint_transforms, skin_weights, bind_matrices_inv):
+        """
+        Apply linear blend skinning to vertices.
+        
+        Args:
+            vertices: (V, 3) vertex positions
+            joint_transforms: (T, J, 4, 4) joint transformation matrices
+            skin_weights: (V, J) per-vertex joint weights
+            bind_matrices_inv: (J, 4, 4) inverse bind matrices
+            
+        Returns:
+            (T, V, 3) deformed vertices
+        """
+        # Compute final transformation matrices
+        transforms = torch.matmul(joint_transforms, bind_matrices_inv)  # (T, J, 4, 4)
+        
+        # Weight and blend transformations
+        weighted_transforms = torch.einsum('vj,tjab->tvab', skin_weights, transforms)  # (T, V, 4, 4)
+        
+        # Apply to vertices
+        vertices_hom = torch.cat([vertices, torch.ones(vertices.shape[0], 1, device=vertices.device)], dim=-1)
+        deformed = torch.matmul(weighted_transforms, vertices_hom.unsqueeze(-1)).squeeze(-1)
+        
+        return deformed[..., :3]
+    
+    def forward_kinematics(self, local_quaternions, parent_indices, root_index = 0):
+        """
+        Compute global joint transformations from local ones.
+        
+        Args:
+            local_quaternions: (B, J, 4) local rotations
+            parent_indices: (J,) parent index for each joint
+            root_index: Root joint index
+            
+        Returns:
+            Tuple of (global_quaternions, global_positions)
+        """
+        B, J = local_quaternions.shape[:2]
+        local_positions = self.rest_local_positions.unsqueeze(0).expand(B, -1, -1)
+        
+        
+        # Initialize storage
+        global_quats = [None] * J
+        global_positions = [None] * J
+        
+        # Build children mapping
+        children = [[] for _ in range(J)]
+        for child_idx in range(J):
+            parent_idx = parent_indices[child_idx]
+            if parent_idx >= 0:
+                children[parent_idx].append(child_idx)
+        
+        # Breadth-first traversal from root
+        queue = deque([root_index])
+        visited = {root_index}
+        
+        # Process root
+        global_quats[root_index] = local_quaternions[:, root_index]
+        global_positions[root_index] = local_positions[:, root_index]
+        
+        while queue:
+            current = queue.popleft()
+            current_quat = global_quats[current]
+            current_pos = global_positions[current]
+            
+            for child in children[current]:
+                if child not in visited:
+                    visited.add(child)
+                    queue.append(child)
+                    
+                    # Transform child to global space
+                    child_quat = quat_multiply(current_quat, local_quaternions[:, child])
+                    child_pos = quat_rotate_vector(current_quat, local_positions[:, child]) + current_pos
+                    
+                    global_quats[child] = child_quat
+                    global_positions[child] = child_pos
+        
+        return torch.stack(global_quats, dim=1), torch.stack(global_positions, dim=1)

animation/optimization.py

@@ -0,0 +1,626 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+
+import os
+import argparse
+import json
+import numpy as np
+import logging
+import glob
+import torch
+import torch.nn.functional as F
+from PIL import Image
+from tqdm import tqdm
+
+from renderer import MeshRenderer3D
+from model import RiggingModel
+from utils.quat_utils import (
+    compute_rest_local_positions, quat_inverse, quat_log, quat_multiply
+)
+from utils.loss_utils import (
+    DepthModule, compute_reprojection_loss, geodesic_loss, root_motion_reg, 
+    calculate_flow_loss, compute_depth_loss_normalized, joint_motion_coherence
+)
+from utils.data_loader import load_model_from_obj_and_rig, prepare_depth
+from utils.save_utils import (
+    save_args, visualize_joints_on_mesh, save_final_video,
+    save_and_smooth_results, visualize_points_on_mesh, save_track_points
+)
+from utils.misc import warmup_then_decay
+from third_partys.co_tracker.save_track import save_track
+
+class AnimationOptimizer:
+    """Main class for animation optimization with video guidance."""
+    
+    def __init__(self, args, device = 'cuda:0'):
+        self.args = args
+        self.device = device
+        self.logger = self._setup_logger()
+        
+        # Training parameters
+        self.reinit_patience_threshold = 20
+        self.loss_divergence_factor = 2.0
+        self.gradient_clip_norm = 1.0
+        
+        # Loss weights
+        self.target_ratios = {
+            'rgb': args.rgb_wt,        
+            'flow': args.flow_wt,    
+            'proj_joint': args.proj_joint_wt,   
+            'proj_vert': args.proj_vert_wt,
+            'depth': args.depth_wt, 
+            'mask': args.mask_wt
+        }
+        self.loss_weights = {
+            'rgb': 1.0,
+            'flow': 1.0,
+            'proj_joint': 1.0,
+            'proj_vert': 1.0, 
+            'depth': 1.0,
+            'mask': 1.0
+        }
+
+    def _setup_logger(self):
+        """Set up logging configuration."""
+        logger = logging.getLogger("animation_optimizer")
+        logger.setLevel(logging.INFO)
+        
+        if not logger.handlers:
+            formatter = logging.Formatter(
+                "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+            )
+            console_handler = logging.StreamHandler()
+            console_handler.setFormatter(formatter)
+            logger.addHandler(console_handler)
+        
+        return logger
+    def _add_file_handler(self, log_path):
+        """Add file handler to logger."""
+        file_handler = logging.FileHandler(log_path)
+        formatter = logging.Formatter("%(asctime)s %(message)s")
+        file_handler.setFormatter(formatter)
+        self.logger.addHandler(file_handler)
+    
+    def _initialize_parameters(self, batch_size, num_joints):
+        """Initialize optimization parameters."""
+        
+        # Fixed first frame quaternions (identity)
+        fixed_quat_0 = torch.zeros((1, num_joints, 4), device=self.device)
+        fixed_quat_0[..., 0] = 1.0
+        
+        # Initialize learnable quaternions for frames 1 to B-1
+        learn_quats_init = torch.zeros((batch_size - 1, num_joints, 4), device=self.device)
+        learn_quats_init[..., 0] = 1.0
+        quats_to_optimize = learn_quats_init.clone().detach().requires_grad_(True)
+        
+        # Initialize global transformations
+        fixed_global_quat_0 = torch.zeros((1, 4), device=self.device)
+        fixed_global_quat_0[:, 0] = 1.0
+        fixed_global_trans_0 = torch.zeros((1, 3), device=self.device)
+        
+        # Initialize learnable global transformations
+        global_quats_init = torch.zeros((batch_size - 1, 4), device=self.device)
+        global_quats_init[:, 0] = 1.0
+        global_trans_init = torch.zeros((batch_size - 1, 3), device=self.device)
+        
+        global_quats = global_quats_init.clone().detach().requires_grad_(True)
+        global_trans = global_trans_init.clone().detach().requires_grad_(True)
+        
+        return quats_to_optimize, global_quats, global_trans, fixed_quat_0, fixed_global_quat_0, fixed_global_trans_0
+    
+    def _setup_optimizer_and_scheduler(self, quats_to_optimize, global_quats, global_trans, n_iters):
+        """Set up optimizer and learning rate scheduler."""
+        
+        base_lr = self.args.warm_lr
+        max_lr = self.args.lr
+        warmup_steps = 20
+        
+        min_lr = self.args.min_lr
+        quat_lr = base_lr # *2
+        
+        optimizer = torch.optim.AdamW([
+            {'params': quats_to_optimize, 'lr': quat_lr},
+            {'params': global_quats, 'lr': quat_lr},
+            {'params': global_trans, 'lr': base_lr}
+        ])
+        
+        scheduler = warmup_then_decay(
+            optimizer=optimizer,
+            total_steps=n_iters,
+            warmup_steps=warmup_steps,
+            max_lr=max_lr,
+            min_lr=min_lr,
+            base_lr=base_lr
+        )
+        
+        return optimizer, scheduler
+    
+    def _compute_smoothness_losses(self, quats_normed, all_global_quats_normed, all_global_trans, model):
+        """Compute various smoothness losses."""
+        
+        # Rotation smoothness loss using geodesic distance
+        theta = geodesic_loss(quats_normed[1:], quats_normed[:-1])
+        rot_smoothness_loss = (theta ** 2).mean()
+        
+        # Second-order rotation smoothness (acceleration)
+        omega = quat_log(quat_multiply(quat_inverse(quats_normed[:-1]), quats_normed[1:]))
+        rot_acc = omega[1:] - omega[:-1]
+        rot_acc_smoothness_loss = rot_acc.pow(2).mean()
+        
+        # Joint motion coherence loss (parent-child relative motion smoothness)
+        joint_coherence_loss = joint_motion_coherence(quats_normed, model.parent_indices)
+    
+        # Root motion regularization
+        root_pos_smooth_loss, root_quat_smooth_loss = root_motion_reg(
+            all_global_quats_normed, all_global_trans
+        )
+        
+        return rot_smoothness_loss, rot_acc_smoothness_loss, joint_coherence_loss, root_pos_smooth_loss + root_quat_smooth_loss
+    
+    def pre_calibrate_loss_weights(self, loss_components, target_ratios=None):
+        """ calibrate loss weights """  
+        loss_for_ratio = {name: loss.detach().clone() for name, loss in loss_components.items()}
+        
+        rgb_loss = loss_for_ratio['rgb'].item()
+        
+        for name, loss_val in loss_for_ratio.items():
+            if name == 'rgb':
+                continue
+                
+            if loss_val > 1e-8:
+                scale_factor = rgb_loss / loss_val.item()
+                target_ratio = target_ratios.get(name, 1.0)
+                new_weight = self.loss_weights.get(name, 1.0) * scale_factor * target_ratio
+                
+                self.loss_weights[name] = new_weight
+
+    def _compute_losses(
+        self,
+        model,
+        renderer,
+        images_batch,
+        tracked_joints_2d,
+        joint_vis_mask,
+        track_verts_2d,
+        vert_vis_mask,
+        sampled_vertex_indices,
+        track_indices,
+        flow_dirs,
+        depth_gt_raw,
+        mask,
+        out_dir,
+        iteration
+    ):
+        """Compute all losses for the optimization."""
+        
+        batch_size = images_batch.shape[0]
+        meshes = [model.get_mesh(t) for t in range(batch_size)]
+        pred_images_all = renderer.render_batch(meshes)
+        
+        # 2D projection losses
+        pred_joints_3d = model.joint_positions
+        proj_joint_loss = compute_reprojection_loss(
+            renderer, joint_vis_mask, pred_joints_3d,
+            tracked_joints_2d, self.args.img_size
+        )
+        
+        pred_points_3d = model.deformed_vertices[0]
+        proj_vert_loss = compute_reprojection_loss(
+            renderer, vert_vis_mask,
+            pred_points_3d[:, sampled_vertex_indices],
+            track_verts_2d[:, track_indices],
+            self.args.img_size
+        )
+        
+        # RGB loss
+        pred_rgb = pred_images_all[..., :3]
+        real_rgb = images_batch[..., :3]
+        diff_rgb_masked = (pred_rgb - real_rgb) * mask.unsqueeze(-1)
+        
+        mse_rgb_num = (diff_rgb_masked ** 2).sum()
+        mse_rgb_den = mask.sum() * 3
+        rgb_loss = mse_rgb_num / mse_rgb_den.clamp_min(1e-8)
+        
+        # Mask loss
+        silhouette_soft = renderer.render_silhouette_batch(meshes).squeeze()
+        mask_loss = F.binary_cross_entropy(silhouette_soft, mask)
+        
+        # Depth losses
+        fragments = renderer.get_rasterization_fragments(meshes)
+        zbuf_depths = fragments.zbuf[..., 0]
+        depth_loss = compute_depth_loss_normalized(depth_gt_raw, zbuf_depths, mask)
+        
+        # Flow losses
+        flow_loss = calculate_flow_loss(flow_dirs, self.device, mask, renderer, model)
+        
+        loss_components = {
+            'rgb': rgb_loss,
+            'proj_joint': proj_joint_loss,
+            'proj_vert': proj_vert_loss,
+            'depth': depth_loss,
+            'flow': flow_loss,
+            'mask': mask_loss
+        }
+        
+        return loss_components
+
+    def optimization(
+        self,
+        images_batch,
+        model,
+        renderer,
+        tracked_joints_2d,
+        joint_vis_mask,
+        track_verts_2d,
+        vert_vis_mask,
+        sampled_vertex_indices,
+        track_indices,
+        flow_dirs,
+        n_iters,
+        out_dir):
+        """
+        Optimize animation parameters with fixed first frame.
+        """
+        torch.autograd.set_detect_anomaly(True)
+        
+        batch_size, _, _, _ = images_batch.shape
+        num_joints = model.joints_rest.shape[0]
+        
+        # Setup output directory and logging
+        os.makedirs(out_dir, exist_ok=True)
+        log_path = os.path.join(out_dir, "optimization.log")
+        self._add_file_handler(log_path)
+        
+        # Initialize parameters
+        (quats_to_optimize, global_quats, global_trans, 
+         fixed_quat_0, fixed_global_quat_0, fixed_global_trans_0) = self._initialize_parameters(batch_size, num_joints)
+        
+        # Setup rest positions and bind matrices
+        rest_local_pos = compute_rest_local_positions(model.joints_rest, model.parent_indices)
+        model.initialize_bind_matrices(rest_local_pos)
+
+        # Setup optimizer and scheduler
+        optimizer, scheduler = self._setup_optimizer_and_scheduler(
+            quats_to_optimize, global_quats, global_trans, n_iters
+        )
+        
+        # Initialize depth module and flow weights
+        depth_module = DepthModule(
+            encoder='vitl',
+            device=self.device,
+            input_size=images_batch.shape[1],
+            fp32=False
+        )
+        
+        # Prepare masks
+        real_rgb = images_batch[..., :3]
+        threshold = 0.95
+        with torch.no_grad():
+            background_mask = (real_rgb > threshold).all(dim=-1)
+            mask = (~background_mask).float()
+        
+        depth_gt_raw = prepare_depth(
+            flow_dirs.replace('flow', 'depth'), real_rgb, self.device, depth_module
+        )
+        
+        # Optimization tracking
+        best_loss = float('inf')
+        patience = 0
+        best_params = None
+        
+        pbar = tqdm(total=n_iters, desc="Optimizing animation")
+
+        for iteration in range(n_iters):
+            # Combine fixed and learnable parameters
+            quats_all = torch.cat([fixed_quat_0, quats_to_optimize], dim=0)
+            
+            # Normalize quaternions
+            reshaped = quats_all.reshape(-1, 4)
+            norm = torch.norm(reshaped, dim=1, keepdim=True).clamp_min(1e-8)
+            quats_normed = (reshaped / norm).reshape(batch_size, num_joints, 4)
+            
+            # Global transformations
+            all_global_quats = torch.cat([fixed_global_quat_0, global_quats], dim=0)
+            all_global_trans = torch.cat([fixed_global_trans_0, global_trans], dim=0)
+            all_global_quats_normed = all_global_quats / torch.norm(
+                all_global_quats, dim=-1, keepdim=True
+            ).clamp_min(1e-8)
+            
+            # Compute smoothness losses
+            (rot_smoothness_loss, rot_acc_smoothness_loss, joint_coherence_loss,
+            root_smooth_loss) = self._compute_smoothness_losses(
+                quats_normed, all_global_quats_normed, all_global_trans, model
+            )
+            
+            # animate model
+            model.animate(quats_normed, all_global_quats_normed, all_global_trans)
+            
+            # Verify first frame hasn't changed
+            verts0 = model.vertices[0]
+            de0 = model.deformed_vertices[0][0]
+            assert torch.allclose(de0, verts0, atol=1e-2), "First frame vertices have changed!"
+            
+            # Compute all losses
+            loss_components = self._compute_losses(
+                model, renderer, images_batch, tracked_joints_2d, joint_vis_mask,
+                track_verts_2d, vert_vis_mask, sampled_vertex_indices, track_indices,
+                flow_dirs, depth_gt_raw, mask, out_dir, iteration
+            )
+            
+            total_smoothness_loss = rot_smoothness_loss + rot_acc_smoothness_loss * 10 
+            
+            if iteration == 0:
+                self.pre_calibrate_loss_weights(loss_components, self.target_ratios)
+
+            total_loss = (
+                loss_components['rgb'] +
+                self.loss_weights['mask'] * loss_components['mask'] +
+                self.loss_weights['flow'] * loss_components['flow'] +
+                self.loss_weights['proj_joint'] * loss_components['proj_joint'] +
+                self.loss_weights['proj_vert'] * loss_components['proj_vert'] +
+                self.loss_weights['depth'] * loss_components['depth'] +
+                self.args.smooth_weight * total_smoothness_loss +
+                self.args.coherence_weight * joint_coherence_loss +
+                self.args.root_smooth_weight * root_smooth_loss
+            )
+            
+            # Optimization step
+            optimizer.zero_grad()
+            total_loss.backward()
+            torch.nn.utils.clip_grad_norm_(
+                [quats_to_optimize, global_quats, global_trans],
+                max_norm=self.gradient_clip_norm
+            )
+            optimizer.step()
+            scheduler.step()
+            
+            # Update progress bar and logging
+            loss_desc = (
+                f"Loss: {total_loss.item():.4f}, "
+                f"RGB: {loss_components['rgb'].item():.4f}, "
+                f"Mask: {self.loss_weights['mask'] * loss_components['mask'].item():.4f}, "
+                f"Flow: {self.loss_weights['flow'] * loss_components['flow'].item():.4f}, "
+                f"Proj_joint: {self.loss_weights['proj_joint'] * loss_components['proj_joint'].item():.4f}, "
+                f"Proj_vert: {self.loss_weights['proj_vert'] * loss_components['proj_vert'].item():.4f}, "
+                f"Depth: {self.loss_weights['depth'] * loss_components['depth'].item():.4f}, "
+                f"Smooth: {self.args.smooth_weight * total_smoothness_loss.item():.4f}, "
+                f"Joint smooth: {self.args.coherence_weight * joint_coherence_loss.item():.4f}, "
+                f"Root smooth: {self.args.root_smooth_weight * root_smooth_loss.item():.4f}"
+            )
+            pbar.set_description(loss_desc)
+            
+            if iteration % 5 == 0:
+                self.logger.info(f"Iter {iteration}: {loss_desc}")
+            
+            # Adaptive reinitialization
+            current_loss = total_loss.item()
+            if current_loss < best_loss:
+                best_loss = current_loss
+                best_params = {
+                    'quats': quats_to_optimize.clone().detach(),
+                    'global_quats': global_quats.clone().detach(),
+                    'global_trans': global_trans.clone().detach()
+                }
+                patience = 0
+            elif (current_loss > best_loss * self.loss_divergence_factor or 
+                  patience > self.reinit_patience_threshold * 2):
+                # Reinitialize with best parameters
+                quats_to_optimize = best_params['quats'].clone().requires_grad_(True)
+                global_quats = best_params['global_quats'].clone().requires_grad_(True)
+                global_trans = best_params['global_trans'].clone().requires_grad_(True)
+                
+                optimizer, scheduler = self._setup_optimizer_and_scheduler(
+                    quats_to_optimize, global_quats, global_trans, n_iters
+                )
+                patience = 0
+                self.logger.info(f'Adaptive reset at iteration {iteration} with best loss: {best_loss:.6f}')
+            else:
+                patience += 1
+            
+            pbar.update(1)
+        
+        pbar.close()
+        
+        # Prepare final results
+        quats_final = torch.cat([fixed_quat_0, best_params['quats']], dim=0)
+        
+        # Final normalization
+        reshaped = quats_final.reshape(-1, 4)
+        norm = torch.norm(reshaped, dim=1, keepdim=True).clamp_min(1e-8)
+        quats_final = (reshaped / norm).reshape(batch_size, num_joints, 4)
+        
+        global_quats_final = torch.cat([fixed_global_quat_0, best_params['global_quats']], dim=0)
+        global_trans_final = torch.cat([fixed_global_trans_0, best_params['global_trans']], dim=0)
+        global_quats_final = global_quats_final / torch.norm(
+            global_quats_final, dim=-1, keepdim=True
+        ).clamp_min(1e-8)
+        
+        return quats_final, global_quats_final, global_trans_final
+
+def load_and_prepare_data(args):
+    """Load and prepare all necessary data for optimization."""
+    
+    # Define paths
+    base_path = f'{args.input_path}/{args.seq_name}'
+    mesh_path = f'{base_path}/objs/mesh.obj'
+    rig_path = f'{base_path}/objs/rig.txt'
+    img_path = f'{base_path}/imgs'
+    flow_dirs = f'{base_path}/flow'
+    
+    # Load model
+    model = load_model_from_obj_and_rig(mesh_path, rig_path, device=args.device)
+    
+    # Load images
+    img_files = sorted(glob.glob(os.path.join(img_path, "*.png")))
+    images = []
+    for f in img_files:
+        img = Image.open(f).convert("RGBA")
+        arr = np.array(img, dtype=np.float32) / 255.0
+        t = torch.from_numpy(arr).to(args.device)
+        images.append(t)
+    
+    images_batch = torch.stack(images, dim=0)
+    
+    return model, images_batch, flow_dirs, img_path
+
+def setup_renderers(args):
+    """Setup multiple renderers for different camera views."""
+    
+    available_views = [
+        "front", "back", "left", "right",
+        "front_left", "front_right", "back_left", "back_right"
+    ]
+    
+    if args.main_renderer not in available_views:
+        raise ValueError(f"Main renderer '{args.main_renderer}' not found in available cameras: {available_views}")
+    
+    main_cam_config = json.load(open(f"utils/cameras/{args.main_renderer}.json"))
+    main_renderer = MeshRenderer3D(args.device, image_size=args.img_size, cam_params=main_cam_config)
+    
+    additional_views = [view.strip() for view in args.additional_renderers.split(',') if view.strip()]
+    if len(additional_views) > 3:
+        print(f"Warning: Only first 3 additional renderers will be used. Got: {additional_views}")
+        additional_views = additional_views[:3]
+    
+    additional_renderers = {}
+    for view_name in additional_views:
+        if view_name in available_views and view_name != args.main_renderer:
+            cam_config = json.load(open(f"utils/cameras/{view_name}.json"))
+            renderer = MeshRenderer3D(args.device, image_size=args.img_size, cam_params=cam_config)
+            additional_renderers[f"{view_name}_renderer"] = renderer
+        elif view_name == args.main_renderer:
+            print(f"Warning: '{view_name}' is already the main renderer, skipping...")
+        elif view_name not in available_views:
+            print(f"Warning: Camera view '{view_name}' not found, skipping...")
+    
+    return main_renderer, additional_renderers
+
+def get_parser():
+    """Create argument parser with all configuration options."""
+    
+    parser = argparse.ArgumentParser(description="3D Rigging Optimization")
+    
+    # Training parameters
+    training_group = parser.add_argument_group('Training')
+    training_group.add_argument("--iter", type=int, default=500, help="Number of training iterations")
+    training_group.add_argument("--img_size", type=int, default=512, help="Image resolution")
+    training_group.add_argument("--device", type=str, default="cuda:0", help="Device to use")
+    training_group.add_argument("--img_fps", type=int, default=15, help="Image frame rate")
+    training_group.add_argument('--main_renderer', type=str, default='front', help='Main renderer camera view (default: front)')
+    training_group.add_argument('--additional_renderers', type=str, default="back, right, left", help='Additional renderer views (max 3), comma-separated (e.g., "back,left,right"). ')
+    
+    # Learning rates
+    lr_group = parser.add_argument_group('Learning Rates')
+    lr_group.add_argument("--lr", type=float, default=2e-3, help="Base learning rate")
+    lr_group.add_argument("--min_lr", type=float, default=1e-5, help="Minimum learning rate")
+    lr_group.add_argument("--warm_lr", type=float, default=1e-5, help="Warmup learning rate")
+    
+    # Loss weights
+    loss_group = parser.add_argument_group('Loss Weights')
+    loss_group.add_argument("--smooth_weight", type=float, default=0.2)
+    loss_group.add_argument("--root_smooth_weight", type=float, default=1.0)
+    loss_group.add_argument("--coherence_weight", type=float, default=10)
+    loss_group.add_argument("--rgb_wt", type=float, default=1.0, help="RGB loss target ratio (relative importance)")
+    loss_group.add_argument("--mask_wt", type=float, default=1.0, help="Mask loss target ratio") 
+    loss_group.add_argument("--proj_joint_wt", type=float, default=1.5, help="Joint projection loss target ratio")
+    loss_group.add_argument("--proj_vert_wt", type=float, default=3.0, help="Point projection loss target ratio")
+    loss_group.add_argument("--depth_wt", type=float, default=0.8, help="Depth loss target ratio")
+    loss_group.add_argument("--flow_wt", type=float, default=0.8, help="Flow loss target ratio")
+
+    # Data and output
+    data_group = parser.add_argument_group('Data and Output')
+    data_group.add_argument("--input_path", type=str, default="inputs")
+    data_group.add_argument("--save_path", type=str, default="results")
+    data_group.add_argument("--save_name", type=str, default="results")
+    data_group.add_argument("--seq_name", type=str, default=None)
+    
+    # Flags
+    flag_group = parser.add_argument_group('Flags')
+    flag_group.add_argument('--gauss_filter', action='store_true', default=False)
+    return parser
+
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+    
+    # Setup output directory
+    out_dir = f'{args.save_path}/{args.seq_name}/{args.save_name}'
+    save_args(args, out_dir)
+    
+    # Initialize optimizer
+    ani_optimizer = AnimationOptimizer(args, device=args.device)
+    
+    # Setup renderers
+    renderer, additional_renderers = setup_renderers(args)
+
+    # Load and prepare data
+    model, images_batch, flow_dirs, img_path = load_and_prepare_data(args)
+    
+    # Setup tracking
+    joint_vis_mask = visualize_joints_on_mesh(model, renderer, args.seq_name, out_dir=out_dir)
+    joint_vis_mask = torch.from_numpy(joint_vis_mask).float().to(args.device)
+    
+    joint_project_2d = renderer.project_points(model.joints_rest)
+    
+    # Setup track paths
+    track_2d_path = img_path.replace('imgs', 'track_2d_joints')
+    os.makedirs(track_2d_path, exist_ok=True)
+    
+    # Load or generate tracks
+    if not os.listdir(track_2d_path):
+        print("Generating joint tracks")
+        tracked_joints_2d = save_track(args.seq_name, joint_project_2d, img_path, track_2d_path, out_dir)
+    else:
+        print("Loading existing joint tracks")
+        tracked_joints_2d = np.load(f'{track_2d_path}/pred_tracks.npy')
+    
+    # Setup point tracking
+    vert_vis_mask = visualize_points_on_mesh(model, renderer, args.seq_name, out_dir=out_dir)
+    vert_vis_mask = torch.from_numpy(vert_vis_mask).float().to(args.device)
+    
+    track_verts_2d, track_indices, sampled_vertex_indices = save_track_points(
+        vert_vis_mask, renderer, model, img_path, out_dir, args
+    )
+    vert_vis_mask = vert_vis_mask[sampled_vertex_indices]
+    
+    # Run optimization
+    print(f"Starting optimization")
+    final_quats, root_quats, root_pos = ani_optimizer.optimization(
+        images_batch=images_batch,
+        model=model,
+        renderer=renderer,
+        tracked_joints_2d=tracked_joints_2d,
+        joint_vis_mask=joint_vis_mask,
+        track_verts_2d=track_verts_2d,
+        vert_vis_mask=vert_vis_mask,
+        sampled_vertex_indices=sampled_vertex_indices,
+        track_indices=track_indices,
+        flow_dirs=flow_dirs,
+        n_iters=args.iter,
+        out_dir=out_dir
+    )
+    
+    # Save results
+    save_and_smooth_results(
+        args, model, renderer, final_quats, root_quats, root_pos,
+        out_dir, additional_renderers, fps=10
+    )
+    
+    print("Optimization completed successfully")
+    save_final_video(args)
+    
+
+if __name__ == "__main__":
+    main()

animation/renderer.py

@@ -0,0 +1,348 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import numpy as np
+import torch
+import cv2
+
+from pytorch3d.structures import join_meshes_as_scene, join_meshes_as_batch, Meshes
+from pytorch3d.renderer import (
+    FoVPerspectiveCameras, look_at_view_transform,
+    RasterizationSettings, MeshRenderer, MeshRasterizer,
+    SoftPhongShader, PointLights, BlendParams, SoftSilhouetteShader
+)
+from utils.loss_utils import compute_visibility_mask_igl
+
+def create_camera_from_blender_params(cam_params, device):
+    """
+    Convert Blender camera parameters to PyTorch3D camera
+    
+    Args:
+        cam_params (dict): Camera parameters from Blender JSON
+        device: Device to create camera on
+    
+    Returns:
+        FoVPerspectiveCameras: Converted camera
+    """
+    # Extract matrix world and convert to rotation and translation
+    matrix_world = torch.tensor(cam_params['matrix_world'], dtype=torch.float32)
+    
+    # Extract field of view (use x_fov, assuming symmetric FOV)
+    fov = cam_params['x_fov'] * 180 / np.pi  # Convert radians to degrees
+
+    rotation_matrix = torch.tensor([
+        [1, 0, 0, 0],
+        [0, 0, 1, 0],
+        [0, -1, 0, 0],
+        [0, 0, 0, 1]
+    ], dtype=torch.float32)
+    
+    # Apply transformations
+    adjusted_matrix = rotation_matrix @ matrix_world
+    world2cam_matrix_tensor = torch.linalg.inv(adjusted_matrix)
+    
+    aligned_matrix = torch.tensor([
+        [-1.0, 0.0, 0.0, 0.0],
+        [0.0, 1.0, 0.0, 0.0],
+        [0.0, 0.0, -1.0, 0.0],
+        [0.0, 0.0, 0.0, 1.0]
+    ], dtype=torch.float32, device=device)
+    world2cam_matrix = aligned_matrix @ world2cam_matrix_tensor.to(device)
+    cam2world_matrix = torch.linalg.inv(world2cam_matrix)
+    
+    # Extract rotation and translation
+    R = cam2world_matrix[:3, :3]
+    T = torch.tensor([
+        world2cam_matrix[0, 3], 
+        world2cam_matrix[1, 3], 
+        world2cam_matrix[2, 3]
+    ], device=device, dtype=torch.float32)
+    
+    return FoVPerspectiveCameras(
+        device=device, 
+        fov=fov, 
+        R=R[None], 
+        T=T[None],
+        znear=0.1,
+        zfar=100.0
+    )
+    
+class MeshRenderer3D:
+    """
+    PyTorch3D mesh renderer with support for various rendering modes.
+    
+    Features:
+    - Standard mesh rendering with Phong shading
+    - Silhouette rendering
+    - Multi-frame batch rendering  
+    - Point projection with visibility computation
+    """
+    def __init__(self, device, image_size=1024, cam_params=None, light_params=None, raster_params=None):
+        self.device = device
+        # Initialize camera
+        self.camera = self._setup_camera(cam_params)
+        
+        # Initialize light
+        self.light = self._setup_light(light_params)
+        
+        # Initialize rasterization settings
+        self.raster_settings = self._setup_raster_settings(raster_params, image_size)
+        self.camera.image_size = self.raster_settings.image_size
+
+        # Initialize renderers
+        self._setup_renderers()
+
+    def _setup_camera(self, cam_params):
+        """Setup camera based on parameters."""
+        if cam_params is None:
+            # Default camera
+            R, T = look_at_view_transform(3.0, 30, 20, at=[[0.0, 1.0, 0.0]])
+            return FoVPerspectiveCameras(device=self.device, R=R, T=T)
+        
+        # Check if Blender parameters
+        if "matrix_world" in cam_params and "x_fov" in cam_params:
+            return create_camera_from_blender_params(cam_params, self.device)
+        else:
+            raise ValueError("Need to provide blender parameters.")
+
+    def _setup_light(self, light_params):
+        """Setup light source."""
+        if light_params is None:
+            return PointLights(device=self.device, location=[[0.0, 0.0, 3.0]])
+        
+        location = [[
+            light_params.get('light_x', 0.0),
+            light_params.get('light_y', 0.0),
+            light_params.get('light_z', 3.0)
+        ]]
+        return PointLights(device=self.device, location=location)
+
+    def _setup_raster_settings(self, raster_params, default_size):
+        """Setup rasterization settings."""
+        if raster_params is None:
+            raster_params = {
+                "image_size": [default_size, default_size],
+                "blur_radius": 0.0,
+                "faces_per_pixel": 1,
+                "bin_size": 0,
+                "cull_backfaces": False
+            }
+        
+        return RasterizationSettings(**raster_params)
+
+    def _setup_renderers(self) -> None:
+        """Initialize main and silhouette renderers."""
+        rasterizer = MeshRasterizer(
+            cameras=self.camera,
+            raster_settings=self.raster_settings
+        )
+        
+        # Main renderer with Phong shading
+        self.renderer = MeshRenderer(
+            rasterizer=rasterizer,
+            shader=SoftPhongShader(
+                device=self.device,
+                cameras=self.camera,
+                lights=self.light
+            )
+        )
+        
+        # Silhouette renderer
+        blend_params = BlendParams(
+            sigma=1e-4,
+            gamma=1e-4,  
+            background_color=(0.0, 0.0, 0.0)
+        )
+        
+        self.silhouette_renderer = MeshRenderer(
+            rasterizer=rasterizer,
+            shader=SoftSilhouetteShader(blend_params=blend_params)
+        )
+
+    def render(self, meshes):
+        """
+        Render meshes with Phong shading.
+        
+        Args:
+            meshes: Single mesh or list of meshes
+            
+        Returns:
+            Rendered images tensor of shape (1, H, W, C)
+        """
+        scene_mesh = self._prepare_scene_mesh(meshes)
+        return self.renderer(scene_mesh)
+
+    def render_batch(self, mesh_list):
+        """
+        Render multiple frames as a batch.
+        
+        Args:
+            mesh_list: List of mesh lists (one per frame)
+            
+        Returns:
+            Batch of rendered images of shape (B, H, W, C)
+        """
+        assert isinstance(mesh_list, list)
+
+        batch_meshes = []
+        for frame_meshes in mesh_list:
+            scene_mesh = self._prepare_scene_mesh(frame_meshes)
+            batch_meshes.append(scene_mesh)
+        
+        batch_mesh = join_meshes_as_batch(batch_meshes)
+        return self.renderer(batch_mesh)
+
+    def get_rasterization_fragments(self, mesh_list):
+        """
+        Get rasterization fragments for batch of meshes.
+        
+        Args:
+            mesh_list: List of mesh lists (one per frame)
+            
+        Returns:
+            Rasterization fragments
+        """
+        assert isinstance(mesh_list, list)
+        
+        batch_meshes = []
+        for frame_meshes in mesh_list:
+            scene_mesh = self._prepare_scene_mesh(frame_meshes)
+            batch_meshes.append(scene_mesh)
+        
+        batch_mesh = join_meshes_as_batch(batch_meshes)
+        return self.renderer.rasterizer(batch_mesh)
+
+    def render_silhouette_batch(self, mesh_list):
+        """
+        Render silhouette masks for multiple frames.
+        
+        Args:
+            mesh_list: List of mesh lists (one per frame)
+            
+        Returns:
+            Batch of silhouette masks of shape (B, H, W, 1)
+        """
+        assert isinstance(mesh_list, list)
+        
+        batch_meshes = []
+        for frame_meshes in mesh_list:
+            scene_mesh = self._prepare_scene_mesh(frame_meshes)
+            batch_meshes.append(scene_mesh)
+        
+        batch_mesh = join_meshes_as_batch(batch_meshes)
+        silhouette = self.silhouette_renderer(batch_mesh)
+        return silhouette[..., 3:]  # Return alpha channel
+
+    def tensor_to_image(self, tensor):
+        """
+        Convert rendered tensor to numpy image array.
+        
+        Args:
+            tensor: Rendered tensor of shape (B, H, W, C)
+            
+        Returns:
+            Numpy array of shape (H, W, 3) with values in [0, 255]
+        """
+        return (tensor[0, ..., :3].cpu().numpy() * 255).astype(np.uint8)
+
+    def project_points(self, points_3d):
+        """
+        Project 3D joints/vertices to 2D image plane
+        
+        Args:
+            points_3d: shape (N, 3) or (B, N, 3) tensor of 3D points
+            
+        Returns:
+            points_2d: shape (N, 2) or (B, N, 2) tensor of 2D projected points
+        """
+        if not torch.is_tensor(points_3d):
+            points_3d = torch.tensor(points_3d, device=self.device, dtype=torch.float32)
+        
+        
+        if len(points_3d.shape) == 2:
+            points_3d = points_3d.unsqueeze(0)  # (1, N, 3)
+        
+        # project points
+        projected = self.camera.transform_points_screen(points_3d, image_size=self.raster_settings.image_size)
+        
+        if projected.shape[0] == 1:
+            projected_points = projected.squeeze(0)[:, :2]
+        else:
+            projected_points = projected[:, :, :2]
+        return projected_points
+    
+    def render_with_points(self, meshes, points_3d, point_radius=3, for_vertices=False):
+        """
+        render the mesh and visualize the joints/vertices on the image
+        
+        Args:
+            meshes: mesh or list of meshes to be rendered
+            points_3d: shape (N, 3) tensor of 3D joints/vertices
+            point_radius: radius of the drawn points
+            for_vertices: if True, compute visibility for vertices, else for joints
+            
+        Returns:
+            Image with joints/vertices drawn, visibility mask
+        """
+        rendered_image = self.render(meshes)
+        
+        # project 3D points to 2D
+        points_2d = self.project_points(points_3d)
+        
+        image_np = rendered_image[0, ..., :3].cpu().numpy()
+        image_with_points = image_np.copy()
+        height, width = image_np.shape[:2]
+        
+        ray_origins = self.camera.get_camera_center()  # (B, 3)
+        ray_origins = np.tile(ray_origins.detach().cpu().numpy(), (points_3d.shape[0], 1))
+
+        verts = meshes.verts_packed().detach().cpu().numpy()
+        faces = meshes.faces_packed().detach().cpu().numpy()
+        
+        ray_dirs = points_3d.detach().cpu().numpy() - ray_origins # calculate ray directions
+        distances = np.linalg.norm(ray_dirs, axis=1)  # distances from camera to points
+        ray_dirs = (ray_dirs.T / distances).T        # normalize to unit vectors
+        
+        vis_mask = compute_visibility_mask_igl(ray_origins, ray_dirs, distances, verts, faces, distance_tolerance=1e-6, for_vertices=for_vertices)
+        
+        # draw points
+        visible_color=(1, 0, 0) # visible points are red
+        invisible_color=(0, 0, 1) # invisible points are blue
+        for i, point in enumerate(points_2d):
+            x, y = int(point[0].item()), int(point[1].item())
+        
+            if 0 <= x < width and 0 <= y < height:
+                point_color = visible_color if vis_mask[i] else invisible_color
+                cv2.circle(image_with_points, (x, y), point_radius, point_color, -1)
+        
+        result = torch.from_numpy(image_with_points).to(self.device)
+        result = result.unsqueeze(0)
+        
+        if rendered_image.shape[-1] == 4:
+            alpha = rendered_image[..., 3:]
+            result = torch.cat([result, alpha], dim=-1)
+        
+        return result, vis_mask
+
+    def _prepare_scene_mesh(self, meshes):
+        """Convert meshes to a single scene mesh."""
+        if isinstance(meshes, Meshes):
+            return meshes
+        elif isinstance(meshes, list):
+            return join_meshes_as_scene(meshes)
+        else:
+            raise ValueError("meshes must be Meshes object or list of Meshes")
+
+
+        

animation/utils/cameras/back.json

@@ -0,0 +1 @@
+{"matrix_world": [[-1.0, -8.742277657347586e-08, -8.742277657347586e-08, 0.0], [-8.742277657347586e-08, 4.371138118131057e-08, 1.0, 2.0], [-8.74227694680485e-08, 1.0, -4.371138828673793e-08, 0.0], [0.0, 0.0, 0.0, 1.0]], "format_version": 6, "max_depth": 5.0, "bbox": [[-0.14632226526737213, -0.15228690207004547, -0.5013949275016785], [0.18149489164352417, 0.24675098061561584, 0.4873228073120117]], "origin": [0.0, 2.0, 0.0], "x_fov": 0.6911112070083618, "y_fov": 0.6911112070083618, "x": [-1.0, -8.742277657347586e-08, -8.74227694680485e-08], "y": [8.742277657347586e-08, -4.371138118131057e-08, -1.0], "z": [8.742277657347586e-08, -1.0, 4.371138828673793e-08]}

animation/utils/cameras/back_left.json

@@ -0,0 +1,64 @@
+{
+  "matrix_world": [
+    [
+      -0.7071067235853873,
+      -1.5015359289272112e-08,
+      -0.7071068387877032,
+      -1.4142136775754064
+    ],
+    [
+      -0.7071068387877031,
+      -1.1886282763606815e-08,
+      0.7071067235853874,
+      1.4142134471707748
+    ],
+    [
+      -1.9022333375140477e-08,
+      1.0,
+      -2.2125928034189e-09,
+      -4.4251856068378e-09
+    ],
+    [
+      0.0,
+      0.0,
+      0.0,
+      1.0
+    ]
+  ],
+  "format_version": 6,
+  "max_depth": 5.0,
+  "bbox": [
+    [
+      -0.14632226526737213,
+      -0.15228690207004547,
+      -0.5013949275016785
+    ],
+    [
+      0.18149489164352417,
+      0.24675098061561584,
+      0.4873228073120117
+    ]
+  ],
+  "origin": [
+    -1.0,
+    1.0,
+    0.0
+  ],
+  "x_fov": 0.6911112070083618,
+  "y_fov": 0.6911112070083618,
+  "x": [
+    -0.7071067235853873,
+    -1.5015359289272112e-08,
+    -0.7071068387877032
+  ],
+  "y": [
+    -0.7071068387877031,
+    -1.1886282763606815e-08,
+    0.7071067235853874
+  ],
+  "z": [
+    -1.9022333375140477e-08,
+    1.0,
+    -2.2125928034189e-09
+  ]
+}

animation/utils/cameras/back_right.json

@@ -0,0 +1,64 @@
+{
+  "matrix_world": [
+    [
+      -0.7071067854026265,
+      -7.240741267677819e-08,
+      0.7071067769704649,
+      1.4142135539409297
+    ],
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+      0.7071067769704653,
+      2.4325415404202744e-08,
+      0.7071067854026294,
+      1.4142135708052588
+    ],
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+      -6.840043892397674e-08,
+      0.9999999999999971,
+      3.399910591950217e-08,
+      6.799821183900434e-08
+    ],
+    [
+      0.0,
+      0.0,
+      0.0,
+      1.0
+    ]
+  ],
+  "format_version": 6,
+  "max_depth": 5.0,
+  "bbox": [
+    [
+      -0.14632226526737213,
+      -0.15228690207004547,
+      -0.5013949275016785
+    ],
+    [
+      0.18149489164352417,
+      0.24675098061561584,
+      0.4873228073120117
+    ]
+  ],
+  "origin": [
+    1.0,
+    1.0,
+    0.0
+  ],
+  "x_fov": 0.6911112070083618,
+  "y_fov": 0.6911112070083618,
+  "x": [
+    -0.7071067854026265,
+    -7.240741267677819e-08,
+    0.7071067769704649
+  ],
+  "y": [
+    0.7071067769704653,
+    2.4325415404202744e-08,
+    0.7071067854026294
+  ],
+  "z": [
+    -6.840043892397674e-08,
+    0.9999999999999971,
+    3.399910591950217e-08
+  ]
+}

animation/utils/cameras/front.json

@@ -0,0 +1 @@
+{"matrix_world": [[1.0, 0.0, 0.0, 0.0], [0.0, -4.371138828673793e-08, -1.0, -2.0], [0.0, 1.0, -4.371138828673793e-08, 0.0], [0.0, 0.0, 0.0, 1.0]], "format_version": 6, "max_depth": 5.0, "bbox": [[-0.14632226526737213, -0.15228690207004547, -0.5013949275016785], [0.18149489164352417, 0.24675098061561584, 0.4873228073120117]], "origin": [0.0, -2.0, 0.0], "x_fov": 0.6911112070083618, "y_fov": 0.6911112070083618, "x": [1.0, 0.0, 0.0], "y": [-0.0, 4.371138828673793e-08, -1.0], "z": [-0.0, 1.0, 4.371138828673793e-08]}

animation/utils/cameras/front_left.json

@@ -0,0 +1,64 @@
+{
+  "matrix_world": [
+    [
+      0.7071068078790848,
+      2.869602372390645e-08,
+      -0.7071067544940086,
+      -1.4142135089880172
+    ],
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+      -0.7071067544940088,
+      -6.21956508517485e-09,
+      -0.7071068078790852,
+      -1.4142136157581704
+    ],
+    [
+      -2.468905024866075e-08,
+      0.9999999999999996,
+      1.589325537842967e-08,
+      3.178651075685934e-08
+    ],
+    [
+      0.0,
+      0.0,
+      0.0,
+      1.0
+    ]
+  ],
+  "format_version": 6,
+  "max_depth": 5.0,
+  "bbox": [
+    [
+      -0.14632226526737213,
+      -0.15228690207004547,
+      -0.5013949275016785
+    ],
+    [
+      0.18149489164352417,
+      0.24675098061561584,
+      0.4873228073120117
+    ]
+  ],
+  "origin": [
+    -1.0,
+    -1.0,
+    0.0
+  ],
+  "x_fov": 0.6911112070083618,
+  "y_fov": 0.6911112070083618,
+  "x": [
+    0.7071068078790848,
+    2.869602372390645e-08,
+    -0.7071067544940086
+  ],
+  "y": [
+    -0.7071067544940088,
+    -6.21956508517485e-09,
+    -0.7071068078790852
+  ],
+  "z": [
+    -2.468905024866075e-08,
+    0.9999999999999996,
+    1.589325537842967e-08
+  ]
+}

animation/utils/cameras/front_right.json

@@ -0,0 +1,64 @@
+{
+  "matrix_world": [
+    [
+      0.7071068078790848,
+      -2.869602372390645e-08,
+      0.7071067544940086,
+      1.4142135089880172
+    ],
+    [
+      0.7071067544940088,
+      -6.21956508517485e-09,
+      -0.7071068078790852,
+      -1.4142136157581704
+    ],
+    [
+      2.468905024866075e-08,
+      0.9999999999999996,
+      1.589325537842967e-08,
+      3.178651075685934e-08
+    ],
+    [
+      0.0,
+      0.0,
+      0.0,
+      1.0
+    ]
+  ],
+  "format_version": 6,
+  "max_depth": 5.0,
+  "bbox": [
+    [
+      -0.14632226526737213,
+      -0.15228690207004547,
+      -0.5013949275016785
+    ],
+    [
+      0.18149489164352417,
+      0.24675098061561584,
+      0.4873228073120117
+    ]
+  ],
+  "origin": [
+    1.0,
+    -1.0,
+    0.0
+  ],
+  "x_fov": 0.6911112070083618,
+  "y_fov": 0.6911112070083618,
+  "x": [
+    0.7071068078790848,
+    -2.869602372390645e-08,
+    0.7071067544940086
+  ],
+  "y": [
+    0.7071067544940088,
+    -6.21956508517485e-09,
+    -0.7071068078790852
+  ],
+  "z": [
+    2.468905024866075e-08,
+    0.9999999999999996,
+    1.589325537842967e-08
+  ]
+}

animation/utils/cameras/left.json

@@ -0,0 +1 @@
+{"matrix_world": [[7.549790126404332e-08, 7.549790126404332e-08, -1.0, -2.0], [-1.0, 5.6999328827277325e-15, -7.549790126404332e-08, 0.0], [0.0, 1.0, 7.549790126404332e-08, 0.0], [0.0, 0.0, 0.0, 1.0]], "format_version": 6, "max_depth": 5.0, "bbox": [[-0.14632226526737213, -0.15228690207004547, -0.5013949275016785], [0.18149489164352417, 0.24675098061561584, 0.4873228073120117]], "origin": [-2.0, 0.0, 0.0], "x_fov": 0.6911112070083618, "y_fov": 0.6911112070083618, "x": [7.549790126404332e-08, -1.0, 0.0], "y": [-7.549790126404332e-08, -5.6999328827277325e-15, -1.0], "z": [1.0, 7.549790126404332e-08, -7.549790126404332e-08]}

animation/utils/cameras/right.json

@@ -0,0 +1 @@
+{"matrix_world": [[7.549790126404332e-08, -7.549790126404332e-08, 1.0, 2.0], [1.0, 5.6999328827277325e-15, -7.549790126404332e-08, 0.0], [0.0, 1.0, 7.549790126404332e-08, 0.0], [0.0, 0.0, 0.0, 1.0]], "format_version": 6, "max_depth": 5.0, "bbox": [[-0.14632226526737213, -0.15228690207004547, -0.5013949275016785], [0.18149489164352417, 0.24675098061561584, 0.4873228073120117]], "origin": [2.0, 0.0, 0.0], "x_fov": 0.6911112070083618, "y_fov": 0.6911112070083618, "x": [7.549790126404332e-08, 1.0, 0.0], "y": [7.549790126404332e-08, -5.6999328827277325e-15, -1.0], "z": [-1.0, 7.549790126404332e-08, -7.549790126404332e-08]}

animation/utils/data_loader.py

@@ -0,0 +1,170 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import os
+import numpy as np
+import torch
+import random
+from pytorch3d.io import load_objs_as_meshes, load_obj
+from pytorch3d.renderer import TexturesAtlas
+from pytorch3d.structures import Meshes
+from model import RiggingModel
+
+def prepare_depth(depth_path, input_frames, device, depth_model):
+    os.makedirs(depth_path, exist_ok=True)
+    depth_path  = f"{depth_path}/depth_gt_raw.pt" 
+    if os.path.exists(depth_path):
+        print("load GT depth...")
+        depth_gt_raw = torch.load(depth_path, map_location=device)
+    else:
+        print("run VideoDepthAnything and save.")
+        with torch.no_grad():
+            depth_gt_raw = depth_model.get_depth_maps(input_frames)
+        torch.save(depth_gt_raw.cpu(), depth_path)
+        depth_gt_raw = depth_gt_raw.to(device)
+    return depth_gt_raw
+
+def normalize_vertices(verts):
+    """Normalize vertices to a unit cube."""
+    vmin, vmax = verts.min(dim=0).values, verts.max(dim=0).values
+    center = (vmax + vmin) / 2.0
+    scale = (vmax - vmin).max()
+    verts_norm = (verts - center) / scale
+    return verts_norm, center, scale
+
+def build_atlas_texture(obj_path, atlas_size, device):
+    """Load OBJ + materials and bake all textures into a single atlas."""
+    verts, faces, aux = load_obj(
+        obj_path,
+        device=device,
+        load_textures=True,
+        create_texture_atlas=True,
+        texture_atlas_size=atlas_size,
+        texture_wrap="repeat",
+    )
+    atlas = aux.texture_atlas  # (F, R, R, 3)
+    verts_norm, _, _ = normalize_vertices(verts)
+    mesh_atlas = Meshes(
+        verts=[verts_norm],
+        faces=[faces.verts_idx],
+        textures=TexturesAtlas(atlas=[atlas]),
+    )
+    return mesh_atlas
+
+def read_rig_file(file_path):
+    """
+    Read rig from txt file, our format is the same as RigNet: 
+    joints joint_name x y z
+    root root_joint_name
+    skin vertex_idx joint_name weight joint_name weight ...
+    hier parent_joint_name child_joint_name
+    """
+    joints = []
+    bones = []
+    joint_names = [] 
+
+    joint_mapping = {}
+    joint_index = 0
+    
+    skinning_data = {}  # Dictionary to store vertex index -> [(joint_idx, weight), ...]
+
+    with open(file_path, 'r') as file:
+        lines = file.readlines()
+
+    for line in lines:
+        parts = line.split()
+        if line.startswith('joints'):
+            name = parts[1]
+            position = [float(parts[2]), float(parts[3]), float(parts[4])]
+            joints.append(position)
+            joint_names.append(name) 
+            joint_mapping[name] = joint_index
+            joint_index += 1
+        elif line.startswith('hier'):
+            parent_joint = joint_mapping[parts[1]]
+            child_joint = joint_mapping[parts[2]]
+            bones.append([parent_joint, child_joint])
+        elif line.startswith('root'):
+            root = joint_mapping[parts[1]]
+        elif line.startswith('skin'):
+            vertex_idx = int(parts[1])
+            
+            if vertex_idx not in skinning_data:
+                skinning_data[vertex_idx] = []
+
+            for i in range(2, len(parts), 2):
+                if i+1 < len(parts):  
+                    joint_name = parts[i]
+                    weight = float(parts[i+1])
+                    
+                    if joint_name in joint_mapping:
+                        joint_idx = joint_mapping[joint_name]
+                        skinning_data[vertex_idx].append((joint_idx, weight))
+
+    return np.array(joints), np.array(bones), root, joint_names, skinning_data
+
+def load_model_from_obj_and_rig(
+    mesh_path: str,
+    rig_path: str,
+    device: str | torch.device = "cuda",
+    use_skin_color: bool = True,
+    atlas_size: int = 8,
+):
+    """Load a 3D model from OBJ and rig files."""
+
+    # 1) read raw mesh
+    raw_mesh = load_objs_as_meshes([mesh_path], device=device)
+    verts_raw = raw_mesh.verts_packed()      # (V,3)
+    faces_idx = raw_mesh.faces_packed()      # (F,3)
+
+    # 2) read rig data
+    joints_np, bones_np, root_idx, joint_names, skinning_data = read_rig_file(rig_path)
+    J = joints_np.shape[0]
+
+    # parent indices, default -1
+    parent_idx = [-1] * J
+    for p, c in bones_np:
+        parent_idx[c] = p
+
+    verts_norm, center, scale = normalize_vertices(verts_raw)
+    joints_t = torch.as_tensor(joints_np, dtype=torch.float32, device=device)
+    joints_norm = (joints_t - center) / scale
+
+    # skin weights tensor (V,J)
+    V = verts_raw.shape[0]
+    skin_weights = torch.zeros(V, J, dtype=torch.float32, device=device)
+    for v_idx, lst in skinning_data.items():
+        for j_idx, w in lst:
+            skin_weights[v_idx, j_idx] = w
+
+    # 3) texture strategy
+    mesh_norm = build_atlas_texture(mesh_path, atlas_size, device)
+    tex = mesh_norm.textures
+
+    # 4) pack into Model class
+    model = RiggingModel(device=device)
+    model.vertices = [mesh_norm.verts_packed()]
+    model.faces = [faces_idx]
+    model.textures = [tex]
+
+    # rig meta
+    model.bones = bones_np  # (B,2)
+    model.parent_indices = parent_idx
+    model.root_index = root_idx
+    model.skin_weights = [skin_weights]
+
+    model.bind_matrices_inv = torch.eye(4, device=device).unsqueeze(0).expand(J, -1, -1).contiguous()
+    model.joints_rest = joints_norm
+
+    return model

animation/utils/loss_utils.py

@@ -0,0 +1,420 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+from third_partys.Video_Depth_Anything.video_depth_anything.video_depth import VideoDepthAnything
+import torch
+import torch.nn as nn
+import numpy as np
+import igl
+import cv2
+import time
+import torch.nn.functional as F
+from utils.quat_utils import quat_inverse, quat_log, quat_multiply, normalize_quaternion
+from pytorch3d.structures import join_meshes_as_scene, join_meshes_as_batch
+import os
+from pathlib import Path
+
+class DepthModule:
+    def __init__(self, encoder='vitl', device='cuda', input_size=518, fp32=False):
+        """
+        Initialize the depth loss module with Video Depth Anything
+        
+        Args:
+            encoder: 'vitl' or 'vits'
+            device: device to run the model on
+            input_size: input size for the model
+            fp32: whether to use float32 for inference
+        """
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.input_size = input_size
+        self.fp32 = fp32
+        
+        # Initialize model configuration
+        model_configs = {
+            'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]},
+            'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
+        }
+        
+        # Load Video Depth Anything model
+        self.video_depth_model = VideoDepthAnything(**model_configs[encoder])
+        self.video_depth_model.load_state_dict(
+            torch.load(f'./third_partys/Video_Depth_Anything/ckpt/video_depth_anything_{encoder}.pth', map_location='cpu'), 
+            strict=True
+        )
+        self.video_depth_model = self.video_depth_model.to(self.device).eval()
+        for param in self.video_depth_model.parameters():
+            param.requires_grad = False 
+    
+    def get_depth_maps(self, frames, target_fps=30):
+        """
+        Get depth maps for video frames
+        """
+        depths, _ = self.video_depth_model.infer_video_depth(
+            frames, 
+            target_fps, 
+            input_size=self.input_size, 
+            device=self.device, 
+            fp32=self.fp32
+        )
+        return depths
+    
+def save_depth_as_images(depth_np, output_dir='./depth_images'):
+    """save depth images"""
+    os.makedirs(output_dir, exist_ok=True)
+    
+    for i, depth_map in enumerate(depth_np):
+        depth_map = depth_map.detach().cpu().numpy()  
+        valid_mask = (depth_map > 0)
+        if not valid_mask.any():
+            continue
+            
+        valid_min = depth_map[valid_mask].min()
+        valid_max = depth_map[valid_mask].max()
+        
+        normalized = np.zeros_like(depth_map)
+        normalized[valid_mask] = 255.0 * (depth_map[valid_mask] - valid_min) / (valid_max - valid_min)
+    
+        depth_img = normalized.astype(np.uint8)
+        
+        cv2.imwrite(os.path.join(output_dir, f'depth_{i:04d}.png'), depth_img)
+        
+    print(f"Save {len(depth_np)} depth images to {output_dir}")
+
+def compute_visibility_mask_igl(ray_origins, ray_dirs, distances, verts, faces, distance_tolerance=1e-6, for_vertices=False):
+    """
+    Compute visibility mask using IGL ray-mesh intersection.
+    """
+    num_rays = ray_origins.shape[0]
+    visibility_mask = np.ones(num_rays, dtype=bool)
+
+    for i in range(num_rays):
+        ray_origin = ray_origins[i].reshape(1, 3)
+        ray_dir = ray_dirs[i].reshape(1, 3)
+        intersections = igl.ray_mesh_intersect(ray_origin, ray_dir, verts, faces)
+        if intersections:
+            # Count intersections that occur before the target point
+            count = sum(1 for h in intersections if h[4] < distances[i] - distance_tolerance)
+            # count=0 → ray completely missed the mesh; count=1 → ray stops exactly at the face containing the joint
+            # count>1 → ray was blocked by other faces along the way
+            if for_vertices:
+                if count != 1:
+                    visibility_mask[i] = False
+            else: # for joints
+                if count > 2:
+                    visibility_mask[i] = False
+
+    return visibility_mask
+
+def compute_reprojection_loss(renderer, vis_mask, predicted_joints, tracked_joints_2d, image_size):
+    """
+    Compute reprojection loss between predicted 3D points and tracked 2D points.
+    """
+    if predicted_joints.dim() != 3:
+        raise ValueError(f"predicted_joints must be 3D tensor, got shape {predicted_joints.shape}")
+    
+    B, J, _ = predicted_joints.shape
+    device = predicted_joints.device
+    
+    # Project 3D joints to 2D screen coordinates
+    projected = renderer.camera.transform_points_screen(
+        predicted_joints, 
+        image_size=[image_size, image_size]
+    )
+    projected_2d = projected[..., :2]  # (B, J, 2)
+   
+    # Convert and validate tracked joints
+    if not isinstance(tracked_joints_2d, torch.Tensor):
+        tracked_joints_2d = torch.from_numpy(tracked_joints_2d).float()
+    tracked_joints_2d = tracked_joints_2d.to(device)
+   
+    if tracked_joints_2d.dim() == 2:
+        tracked_joints_2d = tracked_joints_2d.unsqueeze(0).expand(B, -1, -1)
+
+    vis_mask = vis_mask.to(device).float()
+
+    num_visible = vis_mask.sum()
+    if num_visible == 0:
+        # No visible joints - return zero loss
+        return torch.tensor(0.0, device=device, requires_grad=True)
+    
+    squared_diff = (projected_2d - tracked_joints_2d).pow(2).sum(dim=-1)  # (B, J)
+    
+    vis_mask_expanded = vis_mask.unsqueeze(0)  # (1, J)
+    masked_loss = squared_diff * vis_mask_expanded  # (B, J)
+    per_frame_loss = masked_loss.sum(dim=1) / num_visible  # (B,)
+    final_loss = per_frame_loss.mean()  # scalar
+
+    return final_loss
+
+def geodesic_loss(q1, q2, eps=1e-6):
+    """
+    Compute geodesic distance loss between batches of quaternions for rot smooth loss.
+    """
+    q1_norm = normalize_quaternion(q1, eps=eps)
+    q2_norm = normalize_quaternion(q2, eps=eps)
+   
+    dot_product = (q1_norm * q2_norm).sum(dim=-1, keepdim=True)
+    q2_corrected = torch.where(dot_product < 0, -q2_norm, q2_norm)
+    inner_product = (q1_norm * q2_corrected).sum(dim=-1)
+   
+    # Clamp to valid range for arccos to avoid numerical issues
+    inner_product_clamped = torch.clamp(inner_product, min=-1.0 + eps, max=1.0 - eps)
+    theta = 2.0 * torch.arccos(torch.abs(inner_product_clamped))
+
+    return theta
+
+def root_motion_reg(root_quats, root_pos):
+    return ((root_pos[1:] - root_pos[:-1])**2).mean(), (geodesic_loss(root_quats[1:], root_quats[:-1])**2).mean()
+
+def joint_motion_coherence(quats_normed, parent_idx):
+    """
+    Compute joint motion coherence loss to enforce smooth relative motion between parent-child joints.
+    """
+    coherence_loss = 0
+    
+    for j, parent in enumerate(parent_idx):
+        if parent != -1:  # Skip root joint
+            parent_rot = quats_normed[:, parent]  # (T, 4)
+            child_rot = quats_normed[:, j]        # (T, 4)
+            
+            # Compute relative rotation of child w.r.t. parent's local frame
+            # local_rot = parent_rot^(-1) * child_rot
+            local_rot = quat_multiply(quat_inverse(parent_rot), child_rot)
+            local_rot_velocity = local_rot[1:] - local_rot[:-1]  #  (T-1, 4)
+
+            coherence_loss += local_rot_velocity.pow(2).mean()
+    
+    return coherence_loss
+
+def read_flo_file(file_path):
+    """
+    Read optical flow from .flo format file.
+    """
+    with open(file_path, 'rb') as f:
+        magic = np.fromfile(f, np.float32, count=1)
+        if len(magic) == 0 or magic[0] != 202021.25:
+               raise ValueError(f'Invalid .flo file format: magic number {magic}')
+           
+        w = np.fromfile(f, np.int32, count=1)[0]
+        h = np.fromfile(f, np.int32, count=1)[0]
+        data = np.fromfile(f, np.float32, count=2*w*h)
+        flow = data.reshape(h, w, 2)
+    return flow
+
+def load_optical_flows(flow_dir, num_frames):
+    """
+    Load sequence of optical flow files.
+    """
+    flow_dir = Path(flow_dir)
+    flows = []
+    
+    for i in range(num_frames - 1):
+        flow_path = flow_dir / f'flow_{i:04d}.flo'
+        if flow_path.exists():
+            flow = read_flo_file(flow_path)
+            flows.append(flow)
+        else:
+            raise ValueError("No flow files found")
+    
+    return np.stack(flows, axis=0)
+
+def rasterize_vertex_flow(flow_vertices, meshes, faces, image_size, renderer, eps = 1e-8):
+    """
+    Rasterize per-vertex flow to dense flow field using barycentric interpolation.
+    """
+    B, V, _ = flow_vertices.shape
+    device = flow_vertices.device
+    
+    if isinstance(image_size, int):
+        H = W = image_size
+    else:
+        H, W = image_size
+    
+    batch_meshes = join_meshes_as_batch([join_meshes_as_scene(m) for m in meshes]).to(device)
+    fragments    = renderer.renderer.rasterizer(batch_meshes)
+   
+    pix_to_face = fragments.pix_to_face    # (B, H, W, K)
+    bary_coords = fragments.bary_coords    # (B, H, W, K, 3)
+
+    flow_scene_list = []
+    for mesh_idx in range(B):
+        mesh = meshes[mesh_idx]
+        V_mesh = mesh.verts_packed().shape[0]
+        
+        if V_mesh > flow_vertices.shape[1]:
+            raise ValueError(f"Mesh {mesh_idx} has {V_mesh} vertices but flow has {flow_vertices.shape[1]}")
+        
+        flow_scene_list.append(flow_vertices[mesh_idx, :V_mesh])
+    
+
+    flow_vertices_scene = torch.cat(flow_scene_list, dim=0).to(device)
+    faces_scene = batch_meshes.faces_packed() 
+
+    flow_pred = torch.zeros(B, H, W, 2, device=device)
+    valid = pix_to_face[..., 0] >= 0
+    
+    for b in range(B):
+        b_valid = valid[b]  # (H,W)
+        if torch.count_nonzero(b_valid) == 0:
+            print(f"No valid pixels found for batch {b}")
+            continue
+            
+        valid_indices = torch.nonzero(b_valid, as_tuple=True)
+        h_indices, w_indices = valid_indices
+        
+        face_idxs = pix_to_face[b, h_indices, w_indices, 0]  # (N,)
+        bary = bary_coords[b, h_indices, w_indices, 0]       # (N,3)
+        
+        max_face_idx = faces_scene.shape[0] - 1
+        if face_idxs.max() > max_face_idx:
+            raise RuntimeError(f"Face index {face_idxs.max()} exceeds max {max_face_idx}")
+       
+        face_verts = faces_scene[face_idxs]  # (N, 3)
+        f0, f1, f2 = face_verts.unbind(-1)  # Each (N,)
+       
+        max_vert_idx = flow_vertices_scene.shape[0] - 1
+        if max(f0.max(), f1.max(), f2.max()) > max_vert_idx:
+            raise RuntimeError(f"Vertex index exceeds flow_vertices_scene size {max_vert_idx}")
+       
+        v0_flow = flow_vertices_scene[f0]  # (N, 2)
+        v1_flow = flow_vertices_scene[f1]  # (N, 2)
+        v2_flow = flow_vertices_scene[f2]  # (N, 2)
+        
+        # Interpolate using barycentric coordinates
+        b0, b1, b2 = bary.unbind(-1)  # Each (N,)
+        
+        # Ensure barycentric coordinates sum to 1 (numerical stability)
+        bary_sum = b0 + b1 + b2
+        b0 = b0 / (bary_sum + eps)
+        b1 = b1 / (bary_sum + eps)
+        b2 = b2 / (bary_sum + eps)
+        
+        flow_interpolated = (
+            b0.unsqueeze(-1) * v0_flow + 
+            b1.unsqueeze(-1) * v1_flow + 
+            b2.unsqueeze(-1) * v2_flow
+        )  # (N, 2)
+        
+        # Update flow prediction
+        flow_pred[b, h_indices, w_indices] = flow_interpolated
+    
+    return flow_pred
+
+def calculate_flow_loss(flow_dir, device, mask, renderer, model):
+    """
+    Calculate optical flow loss with improved error handling and flexibility.
+    """
+    if device is None:
+        device = mask.device
+    
+    T = mask.shape[0]
+    H, W = mask.shape[1:3]
+
+    if mask.shape[0] == T:
+        flow_mask = mask[1:] # Use frames 1 to T-1
+    else:
+        flow_mask = mask 
+        
+    flows_np = load_optical_flows(flow_dir, T)
+    flow_gt = torch.from_numpy(flows_np).float().to(device)  # [T-1, H, W, 2]
+    
+    vertices = model.deformed_vertices[0] # (T,V,3)
+    # Project vertices to get 2D flow
+    proj_t  = renderer.project_points(vertices[:-1])    # (T-1,V,2) in pixels
+    proj_tp = renderer.project_points(vertices[1:])
+    vertex_flow  = proj_tp - proj_t                      # (T-1,V,2) Δx,Δy
+
+    meshes = [model.get_mesh(t) for t in range(T)]
+    flow_pred = rasterize_vertex_flow(vertex_flow, meshes, model.faces[0], (H,W), renderer)  # (B,H,W,2)
+    
+    eps = 1e-3
+    diff = (flow_pred - flow_gt) * flow_mask.unsqueeze(-1) # (T-1, H, W, 2)
+    loss = torch.sqrt(diff.pow(2).sum(dim=-1) + eps**2)  # Charbonnier loss
+    loss = loss.sum() / (flow_mask.sum() + 1e-6)
+    
+    return loss
+
+def normalize_depth_from_reference(depth_maps, reference_idx=0, invalid_value=-1.0, invert=False, eps = 1e-8):
+    """
+    Normalize depth maps based on a reference frame with improved robustness.
+    """
+    if depth_maps.dim() != 3:
+        raise ValueError(f"Expected depth_maps with 3 dimensions, got {depth_maps.dim()}")
+    
+    T, H, W = depth_maps.shape
+    device = depth_maps.device
+    
+    reference_depth = depth_maps[reference_idx]
+    valid_mask = (
+        (reference_depth != invalid_value) & 
+        (reference_depth > 1e-8) &  # Avoid very small positive values
+        torch.isfinite(reference_depth)  # Exclude inf/nan
+    )
+
+    valid_values = reference_depth[valid_mask]
+    min_depth = torch.quantile(valid_values, 0.01)  # 1st percentile
+    max_depth = torch.quantile(valid_values, 0.99)  # 99th percentile
+    
+    depth_range = max_depth - min_depth
+    if depth_range < eps:
+        logger.warning(f"Very small depth range ({depth_range:.6f}), using fallback normalization")
+        min_depth = valid_values.min()
+        max_depth = valid_values.max()
+        depth_range = max(max_depth - min_depth, eps)
+    
+    scale = 1.0 / (max_depth - min_depth)
+    offset = -min_depth * scale
+    
+    all_valid_mask = (
+        (depth_maps != invalid_value) & 
+        (depth_maps > eps) &
+        torch.isfinite(depth_maps)
+    )
+
+    normalized_depths = torch.full_like(depth_maps, invalid_value)
+    
+    if all_valid_mask.any():
+        normalized_values = depth_maps[all_valid_mask] * scale + offset
+        
+        if invert:
+            normalized_values = 1.0 - normalized_values
+        
+        normalized_depths[all_valid_mask] = normalized_values
+    
+    return normalized_depths, scale.item(), offset.item()
+
+def compute_depth_loss_normalized(mono_depths, zbuf_depths, mask):
+    """
+    Compute normalized depth loss.
+    """ 
+    device = zbuf_depths.device
+    # Normalize both depth types
+    zbuf_norm, z_scale, z_offset = normalize_depth_from_reference(zbuf_depths)
+    mono_norm, m_scale, m_offset = normalize_depth_from_reference(mono_depths, invert=True)
+    
+    valid_zbuf = (zbuf_norm >= 0) & (zbuf_norm <= 1)
+    valid_mono = (mono_norm >= 0) & (mono_norm <= 1)
+    if mask.dtype != torch.bool:
+        mask = mask > 0.5
+    combined_mask = mask & valid_zbuf & valid_mono
+    
+    num_valid = combined_mask.sum().item()
+    if num_valid == 0:
+        print("No valid pixels for depth loss computation")
+        return torch.tensor(0.0, device=device, requires_grad=True)
+    
+    depth_diff = (zbuf_norm - mono_norm) * combined_mask.float()
+    loss = (depth_diff**2).sum() / num_valid
+    
+    return loss

animation/utils/misc.py

@@ -0,0 +1,34 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+from torch.optim.lr_scheduler import LambdaLR
+
+def warmup_then_decay(optimizer, total_steps, warmup_steps, max_lr=1e-3, min_lr=1e-5, base_lr=1e-5):
+    """
+    Create a learning rate scheduler with warmup followed by decay.
+    """
+    def lr_lambda(current_step):
+        if current_step < warmup_steps:
+            # warmup: min_lr -> max_lr
+            progress = float(current_step) / float(max(1, warmup_steps))
+            # LR(t) = min_lr + (max_lr - min_lr)*progress
+            return (min_lr + (max_lr - min_lr)*progress) / base_lr
+        else:
+            # decay: warmup_steps -> total_steps
+            progress = float(current_step - warmup_steps) / float(max(1, total_steps - warmup_steps))
+            # LR(t) = max_lr + (min_lr - max_lr)*progress
+            return (max_lr + (min_lr - max_lr)*progress) / base_lr
+
+    scheduler = LambdaLR(optimizer, lr_lambda)
+    return scheduler

animation/utils/quat_utils.py

@@ -0,0 +1,179 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import torch
+from typing import List, Tuple, Optional
+
+EPS = 1e-8
+
+def normalize_quaternion(quat: torch.Tensor, eps: float = EPS) -> torch.Tensor:
+    """
+    Normalize quaternions to unit length.
+    
+    Args:
+        quat: Quaternion tensor of shape (..., 4) with (w, x, y, z) format
+        eps: Small value for numerical stability
+        
+    Returns:
+        Normalized quaternions of same shape
+    """
+    norm = torch.norm(quat, dim=-1, keepdim=True)
+    return quat / torch.clamp(norm, min=eps)
+
+def quat_multiply(q1: torch.Tensor, q2: torch.Tensor) -> torch.Tensor:
+    """
+    Multiply two quaternions using Hamilton product.
+    """
+    w1, x1, y1, z1 = torch.unbind(q1, dim=-1)
+    w2, x2, y2, z2 = torch.unbind(q2, dim=-1)
+
+    w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2
+    x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2
+    y = w1 * y2 - x1 * z2 + y1 * w2 + z1 * x2
+    z = w1 * z2 + x1 * y2 - y1 * x2 + z1 * w2
+    
+    return torch.stack((w, x, y, z), dim=-1)
+
+def quat_conjugate(quat: torch.Tensor) -> torch.Tensor:
+    """
+    Compute quaternion conjugate.
+    """
+    w, xyz = quat[..., :1], quat[..., 1:]
+    return torch.cat([w, -xyz], dim=-1)
+
+def quat_inverse(quat: torch.Tensor, eps: float = EPS) -> torch.Tensor:
+    """
+    Compute quaternion inverse.
+    """
+    conjugate = quat_conjugate(quat)
+    norm_squared = torch.sum(quat * quat, dim=-1, keepdim=True)
+    return conjugate / torch.clamp(norm_squared, min=eps)
+
+def quat_log(quat: torch.Tensor, eps: float = 1e-6) -> torch.Tensor:
+    """
+    Compute quaternion logarithm, mapping to rotation vectors (axis-angle).
+    """
+    # quat_norm = normalize_quaternion(quat, eps)
+    q_norm = torch.sqrt(torch.sum(quat * quat, dim=-1, keepdim=True))
+    quat_norm = quat / torch.clamp(q_norm, min=eps)
+    
+    w = quat_norm[..., 0:1]    # Scalar part
+    xyz = quat_norm[..., 1:]   # Vector part
+    
+    xyz_norm = torch.norm(xyz, dim=-1, keepdim=True)
+    w_clamped = torch.clamp(w, min=-1.0 + eps, max=1.0 - eps)
+    
+    # half-angle
+    half_angle = torch.acos(torch.abs(w_clamped))
+    
+    safe_xyz_norm = torch.clamp(xyz_norm, min=eps)
+    
+    # Scale factor
+    scale = torch.where(
+        xyz_norm < eps,
+        torch.ones_like(xyz_norm), 
+        half_angle / safe_xyz_norm 
+    )
+    
+    # Handle quaternion sign ambiguity (q and -q represent same rotation)
+    sign = torch.where(w >= 0, torch.ones_like(w), -torch.ones_like(w))
+    
+    rotation_vector = sign * scale * xyz
+    
+    return rotation_vector
+
+def quat_rotate_vector(quat: torch.Tensor, vec: torch.Tensor) -> torch.Tensor:
+    """
+    Rotate a 3D vector by a quaternion.
+    """
+    q_vec = quat[..., 1:]  # vector part
+    q_w = quat[..., 0:1]   # scalar part
+    
+    cross1 = torch.cross(q_vec, vec, dim=-1)
+    cross2 = torch.cross(q_vec, cross1, dim=-1)
+    
+    # Apply the rotation formula
+    rotated_vec = vec + 2.0 * q_w * cross1 + 2.0 * cross2
+    
+    return rotated_vec
+
+def quat_to_rotation_matrix(quat: torch.Tensor, eps: float = EPS) -> torch.Tensor:
+    """
+    Convert quaternions to rotation matrices.
+    """
+    quat_norm = normalize_quaternion(quat, eps)
+    w, x, y, z = torch.unbind(quat_norm, dim=-1)
+    
+    xx, yy, zz = x * x, y * y, z * z
+    xy, xz, yz = x * y, x * z, y * z
+    wx, wy, wz = w * x, w * y, w * z
+    
+    r00 = 1.0 - 2.0 * (yy + zz)
+    r01 = 2.0 * (xy - wz)
+    r02 = 2.0 * (xz + wy)
+    
+    r10 = 2.0 * (xy + wz)
+    r11 = 1.0 - 2.0 * (xx + zz)
+    r12 = 2.0 * (yz - wx)
+    
+    r20 = 2.0 * (xz - wy)
+    r21 = 2.0 * (yz + wx)
+    r22 = 1.0 - 2.0 * (xx + yy)
+    
+    rotation_matrix = torch.stack([
+        r00, r01, r02,
+        r10, r11, r12,
+        r20, r21, r22
+    ], dim=-1)
+    
+    return rotation_matrix.reshape(quat.shape[:-1] + (3, 3))
+
+def quat_to_transform_matrix(quat: torch.Tensor, pos: torch.Tensor) -> torch.Tensor:
+    """
+    Convert quaternion and position to 4x4 transformation matrix.
+    """
+    # rotation part
+    rotation = quat_to_rotation_matrix(quat)
+    batch_shape = rotation.shape[:-2]
+    
+    # homogeneous transformation matrix
+    transform = torch.zeros(batch_shape + (4, 4), dtype=rotation.dtype, device=rotation.device)
+    transform[..., :3, :3] = rotation
+    transform[..., :3, 3] = pos
+    transform[..., 3, 3] = 1.0
+    
+    return transform
+
+def compute_rest_local_positions(
+    joint_positions: torch.Tensor,
+    parent_indices: List[int]
+) -> torch.Tensor:
+    """
+    Compute local positions relative to parent joints from global joint positions.
+    """
+    
+    num_joints = joint_positions.shape[0]
+    local_positions = torch.zeros_like(joint_positions)
+    
+    for j in range(num_joints):
+        parent_idx = parent_indices[j]
+        
+        if parent_idx >= 0 and parent_idx != j and parent_idx < num_joints:
+            # Child joint: local offset = global_pos - parent_global_pos
+            local_positions[j] = joint_positions[j] - joint_positions[parent_idx]
+        else:
+            # Root joint: use global position as local position
+            local_positions[j] = joint_positions[j]
+    
+    return local_positions

animation/utils/render_first_frame.py

@@ -0,0 +1,93 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+import os
+import json
+import argparse
+from pathlib import Path
+
+import torch
+from pytorch3d.io import load_objs_as_meshes
+from pytorch3d.renderer import TexturesVertex
+from pytorch3d.structures import Meshes
+from PIL import Image
+
+from renderer import MeshRenderer3D
+from utils.save_utils import render_single_mesh
+
+
+def render_mesh_all_cameras(mesh_path, cameras_dir, output_dir="renders", image_size=512, device="cuda:0"):
+    """
+    Render mesh from all camera viewpoints in the cameras directory.
+    
+    Args:
+        mesh_path: Path to OBJ mesh file
+        cameras_dir: Directory containing camera JSON config files
+        output_dir: Output directory for rendered images
+        image_size: Output image size
+        device: Device to use
+    """
+    cameras_dir = Path(cameras_dir)
+    output_dir = Path(output_dir)
+
+    # Find all JSON camera config files
+    json_files = list(cameras_dir.glob("*.json"))
+    if not json_files:
+        print(f"No JSON camera files found in {cameras_dir}")
+        return
+    
+    print(f"Found {len(json_files)} camera configurations")
+    
+    # Render from each camera viewpoint
+    for json_file in json_files:
+        # Load camera config
+        with open(json_file, 'r') as f:
+            cam_params = json.load(f)
+        
+        # Setup renderer for this camera
+        renderer = MeshRenderer3D(device=device, image_size=image_size, cam_params=cam_params)
+        
+        camera_name = json_file.stem
+        output_path = output_dir / f"render_{camera_name}.png"
+        
+        render_single_mesh(renderer, mesh_path, str(output_path))
+    
+    print(f"All renders saved to: {output_dir}")
+
+def main():
+    parser = argparse.ArgumentParser(description="Render a mesh to an image")
+    parser.add_argument('--input_path', type=str, help="base input path")
+    parser.add_argument('--seq_name', type=str, help="sequence name")
+    parser.add_argument("--cameras_dir", default="utils/cameras", help="Camera config JSON file")
+    parser.add_argument("-s", "--size", type=int, default=512, help="Image size")
+    parser.add_argument("-d", "--device", default="cuda:0", help="Device to use")
+    
+    args = parser.parse_args()
+    
+    mesh_path = f'{args.input_path}/{args.seq_name}/objs/mesh.obj'
+    if not os.path.exists(mesh_path):
+        print(f"Error: Mesh file not found: {mesh_path}")
+    output_dir = f'{args.input_path}/{args.seq_name}/first_frames/'
+    os.makedirs(output_dir, exist_ok=True)
+
+    render_mesh_all_cameras(
+        mesh_path=mesh_path,
+        cameras_dir=args.cameras_dir,
+        output_dir=output_dir,
+        image_size=args.size,
+        device=args.device
+    )
+
+if __name__ == "__main__":
+    main()

animation/utils/save_flow.py

@@ -0,0 +1,288 @@
+
+
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+This module processes PNG frame sequences to generate optical flow using PTLFlow,
+with support for visualization and video generation.
+"""
+
+import argparse
+import os
+import subprocess
+import shutil
+import logging
+from pathlib import Path
+from typing import List, Tuple, Optional, Union
+
+import cv2 as cv
+import torch
+import numpy as np
+from tqdm import tqdm
+
+from third_partys.ptlflow.ptlflow.utils import flow_utils
+from third_partys.ptlflow.ptlflow.utils.io_adapter import IOAdapter
+import third_partys.ptlflow.ptlflow as ptlflow
+
+class OpticalFlowProcessor:
+    """Handles optical flow computation and visualization."""
+    
+    def __init__(
+        self,
+        model_name: str = 'dpflow',
+        checkpoint: str = 'sintel',
+        device: Optional[str] = None,
+        resize_to: Optional[Tuple[int, int]] = None
+    ):
+        """
+        Initialize optical flow processor.
+        
+        Args:
+            model_name: Name of the flow model to use
+            checkpoint: Checkpoint/dataset name for the model
+            device: Device to run on (auto-detect if None)
+            resize_to: Optional (width, height) to resize frames
+        """
+        self.model_name = model_name
+        self.checkpoint = checkpoint
+        self.device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
+        self.resize_to = resize_to
+        
+        # Initialize model
+        self.model = ptlflow.get_model(model_name, ckpt_path=checkpoint).to(self.device).eval()
+        print(f"Loaded {model_name} model on {self.device}")
+        
+        self.io_adapter = None
+    
+    def load_frame_sequence(self, frames_dir: Union[str, Path]) -> Tuple[List[np.ndarray], List[Path]]:
+        """
+        Load PNG frame sequence from directory.
+        """
+        frames_dir = Path(frames_dir)
+        
+        if not frames_dir.exists():
+            raise FileNotFoundError(f"Frames directory not found: {frames_dir}")
+        
+        # Find PNG files and sort naturally
+        png_files = list(frames_dir.glob('*.png'))
+        if len(png_files) < 2:
+            raise ValueError(f"Need at least 2 PNG frames, found {len(png_files)} in {frames_dir}")
+        
+        # Natural sorting for proper frame order
+        png_files.sort(key=lambda x: self._natural_sort_key(x.name))
+        
+        frames = []
+        for png_path in tqdm(png_files, desc="Loading frames"):
+            # Load image in color
+            img_bgr = cv.imread(str(png_path), cv.IMREAD_COLOR)
+            
+            if self.resize_to:
+                img_bgr = cv.resize(img_bgr, self.resize_to, cv.INTER_LINEAR)
+        
+            img_rgb = cv.cvtColor(img_bgr, cv.COLOR_BGR2RGB)
+            frames.append(img_rgb)
+        
+        return frames, png_files
+    
+    def _natural_sort_key(self, filename: str) -> List[Union[int, str]]:
+        """Natural sorting key for filenames with numbers."""
+        import re
+        return [int(text) if text.isdigit() else text.lower() 
+                for text in re.split('([0-9]+)', filename)]
+    
+    def compute_optical_flow_sequence(
+        self,
+        frames: List[np.ndarray],
+        flow_vis_dir: Union[str, Path],
+        flow_save_dir: Optional[Union[str, Path]] = None,
+        save_visualizations: bool = True
+    ) -> List[torch.Tensor]:
+        """
+        Compute optical flow for entire frame sequence.
+        """
+        if len(frames) < 2:
+            raise ValueError("Need at least 2 frames for optical flow")
+        
+        flow_vis_dir = Path(flow_vis_dir)
+        flow_save_dir = Path(flow_save_dir) if flow_save_dir else flow_vis_dir
+        
+        H, W = frames[0].shape[:2]
+        
+        # Initialize IO adapter
+        if self.io_adapter is None:
+            self.io_adapter = IOAdapter(self.model, (H, W))
+        
+        flows = []
+        for i in tqdm(range(len(frames) - 1), desc="Computing optical flow"):
+            # Prepare frame pair
+            frame_pair = [frames[i], frames[i + 1]]
+            raw_inputs = self.io_adapter.prepare_inputs(frame_pair)
+            
+            imgs = raw_inputs['images'][0]  # (2, 3, H, W)
+            
+            pair_tensor = torch.stack((imgs[0:1], imgs[1:2]), dim=1).squeeze(0)  # (1, 2, 3, H, W)
+            pair_tensor = pair_tensor.to(self.device, non_blocking=True).contiguous()
+        
+            with torch.no_grad():
+                flow_result = self.model({'images': pair_tensor.unsqueeze(0)})
+                flow = flow_result['flows'][0]  # (1, 2, H, W)
+            
+            flows.append(flow)
+            
+            if save_visualizations:
+                self._save_flow_outputs(flow, i, flow_vis_dir, flow_save_dir)
+             
+        return flows
+    
+    def _save_flow_outputs(
+        self,
+        flow_tensor: torch.Tensor,
+        frame_idx: int,
+        viz_dir: Path,
+        flow_dir: Path
+    ) -> None:
+        """Save flow outputs in both .flo and visualization formats."""
+        # Save raw flow (.flo format)
+        flow_hw2 = flow_tensor[0]  # (2, H, W)
+        flow_np = flow_hw2.permute(1, 2, 0).cpu().numpy()  # (H, W, 2)
+        
+        flow_path = flow_dir / f'flow_{frame_idx:04d}.flo'
+        flow_utils.flow_write(flow_path, flow_np)
+        
+        # Save visualization
+        flow_rgb = flow_utils.flow_to_rgb(flow_tensor)[0]  # Remove batch dimension
+        
+        if flow_rgb.dim() == 4:  # (Npred, 3, H, W)
+            flow_rgb = flow_rgb[0]
+        
+        flow_rgb_np = (flow_rgb * 255).byte().permute(1, 2, 0).cpu().numpy()  # (H, W, 3)
+        viz_bgr = cv.cvtColor(flow_rgb_np, cv.COLOR_RGB2BGR)
+        
+        viz_path = viz_dir / f'flow_viz_{frame_idx:04d}.png'
+        cv.imwrite(str(viz_path), viz_bgr)
+
+def create_flow_video(
+    image_dir: Union[str, Path],
+    output_filename: str = 'flow.mp4',
+    fps: int = 10,
+    pattern: str = 'flow_viz_*.png',
+    cleanup_temp: bool = True
+) -> bool:
+    """
+    Create MP4 video from flow visualization images.
+    """
+    image_dir = Path(image_dir)
+    
+    if not image_dir.exists():
+        print(f"Image directory not found: {image_dir}")
+    
+    image_files = sorted(image_dir.glob(pattern))
+    if not image_files:
+        print(f"No images found matching pattern '{pattern}' in {image_dir}")
+    
+    temp_dir = image_dir / 'temp_sequence'
+    temp_dir.mkdir(exist_ok=True)
+    
+    try:
+        # Copy files with sequential naming
+        for i, img_file in enumerate(image_files):
+            temp_name = temp_dir / f'frame_{i:05d}.png'
+            shutil.copy2(img_file, temp_name)
+        
+        # Create video using ffmpeg
+        output_path = image_dir / output_filename
+        
+        cmd = [
+            'ffmpeg', '-y',
+            '-framerate', str(fps),
+            '-i', str(temp_dir / 'frame_%05d.png'),
+            '-c:v', 'libx264',
+            '-pix_fmt', 'yuv420p',
+            str(output_path)
+        ]
+        
+        subprocess.run(
+            cmd, 
+            capture_output=True, 
+            text=True, 
+            check=True
+        )
+        return True
+    except Exception as e:
+        print(f"Video creation failed: {e}")
+        return False
+    finally:
+        if cleanup_temp and temp_dir.exists():
+            shutil.rmtree(temp_dir)
+
+def main(
+    frames_dir: Union[str, Path],
+    flow_vis_dir: Union[str, Path] = 'flow_out',
+    flow_save_dir: Optional[Union[str, Path]] = None,
+    resize_to: Optional[Tuple[int, int]] = None,
+    model_name: str = 'dpflow',
+    checkpoint: str = 'sintel'
+) -> bool:
+
+    # Initialize processor
+    processor = OpticalFlowProcessor(
+        model_name=model_name,
+        checkpoint=checkpoint,
+        resize_to=resize_to
+    )
+    
+    # Load frames
+    frames, png_paths = processor.load_frame_sequence(frames_dir)
+    
+    # Compute optical flow
+    flows = processor.compute_optical_flow_sequence(
+        frames=frames,
+        flow_vis_dir=flow_vis_dir,
+        flow_save_dir=flow_save_dir,
+        save_visualizations=True
+    )
+    
+    # Create video
+    create_flow_video(flow_vis_dir)
+
+def get_parser():
+    parser = argparse.ArgumentParser(description="Optical flow inference on frame sequences")
+    
+    parser.add_argument('--input_path', type=str, help="base input path")
+    parser.add_argument('--seq_name', type=str, help="sequence name")
+    parser.add_argument('--model_name', type=str, default='dpflow', help="Optical flow model to use")
+    parser.add_argument('--checkpoint', type=str, default='sintel', help="Model checkpoint/dataset name")
+    parser.add_argument('--resize_width', type=int, default=None, help="Resize frame width (must specify both width and height)")
+    parser.add_argument('--resize_height', type=int, default=None, help="Resize frame height (must specify both width and height)")
+    parser.add_argument('--fps', type=int, default=10, help="Frame rate for output video")
+
+    return parser
+
+if __name__ == '__main__':
+    parser = get_parser()
+    args = parser.parse_args()
+    
+    # Path
+    frames_dir = f'{args.input_path}/{args.seq_name}/imgs'
+    flow_vis_dir = frames_dir.replace("imgs", "flow_vis")
+    flow_save_dir = frames_dir.replace("imgs", "flow")
+
+    os.makedirs(flow_vis_dir, exist_ok=True)
+    os.makedirs(flow_save_dir, exist_ok=True)
+    
+    # Prepare resize parameter
+    resize_to = None
+    if args.resize_width and args.resize_height:
+        resize_to = (args.resize_width, args.resize_height)
+    
+    # Process optical flow
+    success = main(
+        frames_dir=frames_dir,
+        flow_vis_dir=flow_vis_dir,
+        flow_save_dir=flow_save_dir,
+        resize_to=resize_to,
+        model_name=args.model_name,
+        checkpoint=args.checkpoint
+    )
+    
+    print("Optical flow processing completed successfully")

animation/utils/save_utils.py

@@ -0,0 +1,374 @@
+#  Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
+# 
+#  Licensed under the Apache License, Version 2.0 (the "License");
+#  you may not use this file except in compliance with the License.
+#  You may obtain a copy of the License at
+# 
+#  http://www.apache.org/licenses/LICENSE-2.0
+# 
+#  Unless required by applicable law or agreed to in writing, software
+#  distributed under the License is distributed on an "AS IS" BASIS,
+#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#  See the License for the specific language governing permissions and
+#  limitations under the License.
+
+from pytorch3d.io import load_obj
+from pytorch3d.renderer import TexturesAtlas
+from pytorch3d.structures import Meshes
+
+import os
+import torch
+import json
+import numpy as np
+from tqdm import tqdm
+from pathlib import Path
+import subprocess
+from PIL import Image
+from scipy.ndimage import gaussian_filter1d
+from third_partys.co_tracker.save_track import save_track
+
+def render_single_mesh(renderer, mesh_path, out_path="render_result.png", atlas_size=8):
+    """
+    Test render a single mesh and save the result.
+    """
+    device = renderer.device 
+
+    verts, faces, aux = load_obj(
+        mesh_path,
+        device=device,
+        load_textures=True,
+        create_texture_atlas=True,
+        texture_atlas_size=atlas_size,
+        texture_wrap="repeat"
+    )
+    atlas = aux.texture_atlas            # (F, atlas_size, atlas_size, 3)
+
+    vmin, vmax = verts.min(0).values, verts.max(0).values
+    center = (vmax + vmin) / 2.
+    scale  = (vmax - vmin).max()
+    verts  = (verts - center) / scale
+
+    mesh_norm = Meshes(
+        verts=[verts],
+        faces=[faces.verts_idx],
+        textures=TexturesAtlas(atlas=[atlas])
+    )
+    with torch.no_grad():
+        rendered = renderer.render(mesh_norm)  # shape=[1, H, W, 4]
+    
+    rendered_img = renderer.tensor_to_image(rendered)
+    
+    pil_img = Image.fromarray(rendered_img)
+    pil_img.save(out_path)
+    print(f"Saved render to {out_path}")
+
+def apply_gaussian_smoothing(data, sigma = 1.0, preserve_first_frame = True, eps = 1e-8):
+    """
+    Apply Gaussian smoothing along the time axis with quaternion normalization.
+    """
+    smoothed = gaussian_filter1d(data, sigma=sigma, axis=0)
+    
+    # Preserve first frame if requested
+    if preserve_first_frame and data.shape[0] > 0:
+        smoothed[0] = data[0]
+
+    if data.shape[-1] == 4:
+        norms = np.linalg.norm(smoothed, axis=-1, keepdims=True)
+        smoothed = smoothed / np.maximum(norms, eps)
+    
+    return smoothed
+
+def render_single_view_sequence(quats, root_quats, root_pos, renderer, model, output_dir, view_name, fps = 25):
+    """
+    Render animation sequence from a single viewpoint.
+    """
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    T = quats.shape[0]
+
+    model.animate(quats, root_quats, root_pos)
+
+    for i in tqdm(range(T), desc=f"Rendering {view_name}"):
+        mesh = model.get_mesh(i)
+        rendered = renderer.render(mesh)
+
+        img_array = renderer.tensor_to_image(rendered)
+        img = Image.fromarray(img_array)
+        
+        frame_path = output_dir / f"{view_name}_frame_{i:04d}.png"
+        img.save(frame_path)
+
+    # Create video
+    video_path = output_dir / f"{view_name}_output_video.mp4"
+    cmd = f"ffmpeg -y -framerate {fps} -i {output_dir}/{view_name}_frame_%04d.png -c:v libx264 -pix_fmt yuv420p {video_path}"
+    subprocess.call(cmd, shell=True)
+
+def save_and_smooth_results(args, model, renderer, final_quats, root_quats, root_pos, out_dir, additional_renderers = None, load_pt = False, sigma = 1.0, fps = 25):
+    """
+    Save and smooth animation results with multi-view rendering.
+    """
+    device = final_quats.device
+    T = final_quats.shape[0]
+    # Save Raw Results
+    if not load_pt:
+        raw_dir = os.path.join(out_dir, "raw")
+        os.makedirs(raw_dir, exist_ok=True)
+        
+        torch.save(final_quats, os.path.join(raw_dir, "local_quats.pt"))
+        torch.save(root_quats, os.path.join(raw_dir, "root_quats.pt"))
+        torch.save(root_pos, os.path.join(raw_dir, "root_pos.pt"))
+        if hasattr(model, 'rest_local_positions'):
+            torch.save(model.rest_local_positions, os.path.join(raw_dir, "rest_local_positions.pt"))
+        
+        print(f"Saved raw motion to {raw_dir}")
+        
+    quats_np = final_quats.cpu().numpy()
+    root_quats_np = root_quats.cpu().numpy()
+    root_pos_np = root_pos.cpu().numpy()
+
+    # Apply Gaussian smoothing if enabled
+    if args.gauss_filter:
+        print(f"Applying Gaussian smoothing (sigma={sigma})")
+        
+        smooth_quats_np = apply_gaussian_smoothing(
+            quats_np, sigma=sigma, preserve_first_frame=True
+        )
+        smooth_root_quats_np = apply_gaussian_smoothing(
+            root_quats_np, sigma=sigma, preserve_first_frame=True
+        )
+        smooth_root_pos_np = apply_gaussian_smoothing(
+            root_pos_np, sigma=sigma, preserve_first_frame=True
+        )
+        smooth_dir = os.path.join(out_dir, "smoothed")
+        os.makedirs(smooth_dir, exist_ok=True)
+        save_dir = smooth_dir
+      
+    else:
+        smooth_quats_np = quats_np
+        smooth_root_quats_np = root_quats_np
+        smooth_root_pos_np = root_pos_np
+        save_dir = raw_dir
+    
+    smooth_quats = torch.tensor(smooth_quats_np, dtype=torch.float32, device=device)
+    smooth_root_quats = torch.tensor(smooth_root_quats_np, dtype=torch.float32, device=device)
+    smooth_root_pos = torch.tensor(smooth_root_pos_np, dtype=torch.float32, device=device)
+    
+    # Render Sequences
+    if not load_pt and args.gauss_filter:
+        smooth_dir_path = Path(smooth_dir)
+        torch.save(smooth_quats, smooth_dir_path / "local_quats.pt")
+        torch.save(smooth_root_quats, smooth_dir_path / "root_quats.pt")
+        torch.save(smooth_root_pos, smooth_dir_path / "root_pos.pt")
+        print(f"Saved smoothed motion to {smooth_dir}")
+    
+    # Render main view
+    print(f"Rendering {args.main_renderer} view ({T} frames)")
+    render_single_view_sequence(
+        smooth_quats, smooth_root_quats, smooth_root_pos,
+        renderer, model, save_dir, args.main_renderer, fps
+    )
+    
+    # Render additional views if provided
+    if additional_renderers:
+        for renderer_key, view_renderer in additional_renderers.items():
+            view_name = renderer_key.replace("_renderer", "")
+            render_single_view_sequence(
+                smooth_quats, smooth_root_quats, smooth_root_pos,
+                view_renderer, model, save_dir, view_name, fps
+            )
+
+def save_args(args, output_dir, filename="config.json"):
+    args_dict = vars(args)
+    os.makedirs(output_dir, exist_ok=True)
+
+    config_path = os.path.join(output_dir, filename)
+    with open(config_path, 'w') as f:
+        json.dump(args_dict, f, indent=4)
+
+def visualize_joints_on_mesh(model, renderer, seq_name, out_dir):
+    """
+    Render mesh with joint visualizations and return visibility mask.
+    """
+    joints_2d = renderer.project_points(model.joints_rest)
+   
+    mesh = model.get_mesh()
+    image_with_joints, vis_mask = renderer.render_with_points(mesh, model.joints_rest)
+    image_np = image_with_joints[0].cpu().numpy()
+    if image_np.shape[2] == 4:
+        image_rgb = image_np[..., :3]
+    else:
+        image_rgb = image_np
+    if image_rgb.max() <= 1.0:
+        image_rgb = (image_rgb * 255).astype(np.uint8)
+    img = Image.fromarray(image_rgb)
+    output_path = f"{out_dir}/mesh_with_joints_{seq_name}_visible.png"
+    img.save(output_path)
+    return vis_mask
+
+def visualize_points_on_mesh(model, renderer, seq_name, out_dir):
+    """
+    Render mesh with point visualizations and return visibility mask.
+    """
+    points_2d = renderer.project_points(model.vertices[0])
+
+    mesh = model.get_mesh()
+    image_with_points, vis_mask = renderer.render_with_points(mesh, model.vertices[0], for_vertices=True)
+    image_np = image_with_points[0].cpu().numpy()
+    if image_np.shape[2] == 4:
+        image_rgb = image_np[..., :3]
+    else:
+        image_rgb = image_np
+    if image_rgb.max() <= 1.0:
+        image_rgb = (image_rgb * 255).astype(np.uint8)
+    img = Image.fromarray(image_rgb)
+    output_path = f"{out_dir}/mesh_with_verts_{seq_name}_visible.png"
+    img.save(output_path)
+    return vis_mask
+
+def save_track_points(point_vis_mask, renderer, model, img_path, out_dir, args):
+    """
+    Save and track selected points on the mesh with intelligent sampling.
+    """
+
+    vertex_project_2d = renderer.project_points(model.vertices[0])
+    visible_indices = torch.where(point_vis_mask)[0]
+    
+    track_2d_point_path = img_path.replace('imgs', 'track_2d_verts')
+    os.makedirs(track_2d_point_path, exist_ok=True)
+    
+    num_visible = len(visible_indices)
+    MAX_VISIBLE_POINTS = 15000
+    MAX_SAMPLE_POINTS = 4000
+    
+    # Determine tracking strategy
+    tracking_mode = "full" if num_visible <= MAX_VISIBLE_POINTS else "sampled"
+    
+    if not os.listdir(track_2d_point_path):
+        # Generate new tracking data
+        if tracking_mode == "full":
+            print(f"Saving tracks for all visible vertices (count: {num_visible})")
+            
+            # Track all visible points
+            visible_vertex_project_2d = vertex_project_2d[visible_indices]
+            track_2d_point = save_track(
+                args.seq_name, visible_vertex_project_2d, img_path, 
+                track_2d_point_path, out_dir, for_point=True
+            )
+            
+            np.save(f'{track_2d_point_path}/visible_indices.npy', 
+                    visible_indices.cpu().numpy())
+            
+            # Sample subset for final use
+            num_sample = min(MAX_SAMPLE_POINTS, num_visible)
+            sampled_local_indices = torch.randperm(num_visible)[:num_sample]
+            sampled_vertex_indices = visible_indices[sampled_local_indices]
+            np.save(f'{track_2d_point_path}/sampled_indices.npy', 
+                    sampled_vertex_indices.cpu().numpy())
+            
+        else:
+            print(f"Too many visible vertices ({num_visible} > {MAX_VISIBLE_POINTS}), "
+                    f"tracking only {MAX_SAMPLE_POINTS} sampled vertices")
+            
+            # Sample points directly from visible set
+            num_sample = min(MAX_SAMPLE_POINTS, num_visible)
+            sampled_local_indices = torch.randperm(num_visible)[:num_sample]
+            sampled_vertex_indices = visible_indices[sampled_local_indices]
+            
+            # Track only sampled points
+            sampled_vertex_project_2d = vertex_project_2d[sampled_vertex_indices]
+            track_2d_point = save_track(
+                args.seq_name, sampled_vertex_project_2d, img_path, 
+                track_2d_point_path, out_dir, for_point=True
+            )
+            
+            np.save(f'{track_2d_point_path}/visible_indices.npy', 
+                    visible_indices.cpu().numpy())
+            np.save(f'{track_2d_point_path}/sampled_indices.npy', 
+                    sampled_vertex_indices.cpu().numpy())
+    
+    else:
+        # Load existing tracking data
+        print("Loading existing vertex tracks")
+        track_2d_point = np.load(f'{track_2d_point_path}/pred_tracks.npy')
+        
+        visible_indices = np.load(f'{track_2d_point_path}/visible_indices.npy')
+        visible_indices = torch.from_numpy(visible_indices).long().to(args.device)
+        
+        sampled_vertex_indices = np.load(f'{track_2d_point_path}/sampled_indices.npy')
+        sampled_vertex_indices = torch.from_numpy(sampled_vertex_indices).long().to(args.device)
+    
+    track_2d_point = torch.from_numpy(track_2d_point).float().to(args.device)
+    
+    # Create index mapping for tracking data
+    if tracking_mode == "full":
+        # Map from original vertex indices to positions in tracking data
+        vertex_to_track_idx = {idx.item(): i for i, idx in enumerate(visible_indices)}
+        
+        track_indices = torch.tensor(
+            [vertex_to_track_idx[idx.item()] for idx in sampled_vertex_indices], 
+            device=args.device, dtype=torch.long
+        )
+    else:
+        # Direct mapping for sampled-only tracking
+        track_indices = torch.arange(len(sampled_vertex_indices), 
+                                    device=args.device, dtype=torch.long)
+    
+    return track_2d_point, track_indices, sampled_vertex_indices
+
+def save_final_video(args):
+
+    additional_views = [view.strip() for view in args.additional_renderers.split(',') if view.strip()]
+    if len(additional_views) > 3:
+        additional_views = additional_views[:3]
+    additional_views = [view for view in additional_views if view != args.main_renderer]
+    
+    save_dir = 'raw' if not args.gauss_filter else 'smoothed'
+    import subprocess
+    cmd = (
+        f'ffmpeg '
+        f'-i {args.input_path}/{args.seq_name}/input.mp4 '
+        f'-i {args.save_path}/{args.seq_name}/{args.save_name}/{save_dir}/{args.main_renderer}_output_video.mp4 '
+        '-filter_complex "'
+        '[0:v][1:v]hstack=inputs=2[stacked]; '
+        '[stacked]drawtext=fontfile=/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf:text=\'gt\':x=(w/4-text_w/2):y=20:fontsize=24:fontcolor=white:box=1:boxcolor=black:boxborderw=10, '
+        f'drawtext=fontfile=/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf:text=\'ours\':x=(3*w/4-text_w/2):y=20:fontsize=24:fontcolor=white:box=1:boxcolor=black:boxborderw=10" '
+        f'-c:a copy {args.save_path}/{args.seq_name}/{args.save_name}/concat_output.mp4'
+    )
+
+    subprocess.call(cmd, shell=True)
+    cmd = (
+        f'ffmpeg '
+        f'-i {args.input_path}/{args.seq_name}/input.mp4 '
+        f'-i {args.save_path}/{args.seq_name}/{args.save_name}/{save_dir}/{args.main_renderer}_output_video.mp4 '
+        f'-i {args.save_path}/{args.seq_name}/{args.save_name}/{save_dir}/{additional_views[0]}_output_video.mp4 '
+        f'-i {args.save_path}/{args.seq_name}/{args.save_name}/{save_dir}/{additional_views[1]}_output_video.mp4 '
+        f'-i {args.save_path}/{args.seq_name}/{args.save_name}/{save_dir}/{additional_views[2]}_output_video.mp4 '
+        '-filter_complex "'
+        '[0:v][1:v][2:v][3:v][4:v]hstack=inputs=5[stacked]; '
+        '[stacked]drawtext=fontfile=/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf:text=\'gt\':x=(w/10-text_w/2):y=20:fontsize=24:fontcolor=white:box=1:boxcolor=black:boxborderw=10, '
+        f'drawtext=fontfile=/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf:text=\'{args.main_renderer}\':x=(3*w/10-text_w/2):y=20:fontsize=24:fontcolor=white:box=1:boxcolor=black:boxborderw=10, '
+        f'drawtext=fontfile=/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf:text=\'{additional_views[0]}\':x=(5*w/10-text_w/2):y=20:fontsize=24:fontcolor=white:box=1:boxcolor=black:boxborderw=10, '
+        f'drawtext=fontfile=/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf:text=\'{additional_views[1]}\':x=(7*w/10-text_w/2):y=20:fontsize=24:fontcolor=white:box=1:boxcolor=black:boxborderw=10, '
+        f'drawtext=fontfile=/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf:text=\'{additional_views[2]}\':x=(9*w/10-text_w/2):y=20:fontsize=24:fontcolor=white:box=1:boxcolor=black:boxborderw=10" '
+        f'-c:a copy {args.save_path}/{args.seq_name}/{args.save_name}/concat_output_4view.mp4'
+    )
+    subprocess.call(cmd, shell=True)
+
+def load_motion_data(motion_dir, device="cuda:0"):
+    """
+    Load saved motion data.
+    """
+    local_quats = torch.load(os.path.join(motion_dir, "local_quats.pt"), map_location=device)
+    root_quats = torch.load(os.path.join(motion_dir, "root_quats.pt"), map_location=device)
+    root_pos = torch.load(os.path.join(motion_dir, "root_pos.pt"), map_location=device)
+    
+    # Load rest positions if available (for reference)
+    rest_pos_path = os.path.join(motion_dir, "rest_local_positions.pt")
+    if os.path.exists(rest_pos_path):
+        rest_positions = torch.load(rest_pos_path, map_location=device)
+    else:
+        rest_positions = None
+        print("Warning: rest_local_positions.pt not found, model should have them initialized")
+    
+    return local_quats, root_quats, root_pos, rest_positions