Update files

.gitignore

@@ -3,7 +3,11 @@
 *.pth
 *.ckpt
 
+**/__pycache__/
+.gradio/
+
 tmp/
-debug/ 
+debug/
+datasets/PartNet
 outputs/
 evaluations/

README.md

@@ -1,10 +1,176 @@
----
-title: FreeArt3D
-emoji: 🧩
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: "5.34.2"
-app_file: app.py
-pinned: false
----
+<h1 align="center">FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusion</h1> 
+<h3 align="center">SIGGRAPH Asia 2025</h3> 
+
+<p align="center"><a href="https://czzzzh.github.io/FreeArt3D/FreeArt3D.pdf"><img src='https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white' alt='arXiv'></a>
+<a href="https://czzzzh.github.io/FreeArt3D"><img src='https://img.shields.io/badge/Project_Page-Website-green?logo=googlechrome&logoColor=white' alt='Project Page'></a>
+<a href="https://huggingface.co/spaces/MorPhLingXD/FreeArt3D"><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Live_Demo-blue'></a>
+</p>
+
+
+Official Implementation for ***FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusion***
+
+<p align="center"><img src="assets/teaser.png" width="100%"></p>
+
+
+
+## 🔔 Updates
+[10/29/2025] We released our code, dataset and results!
+
+
+
+
+## 🚧 TODO List 
+- [ ] Release test data on Objaverse dataset
+
+
+
+## 🚀 Quick Start
+1. Setup the environment
+
+   ```bash
+   conda create -n fa3d python=3.10
+   conda activate fa3d
+   bash setup.sh
+   ```
+
+   **Note:** If you need to use different torch or cudatoolkit version (for correctly building other libraries), please also install the compatible version of [kaolin](https://github.com/NVIDIAGameWorks/kaolin)
+
+2. Download the checkpoint of [GIM-DKM](https://github.com/xuelunshen/gim) from [Google Drive](https://drive.google.com/file/d/1iYmEctoA-a3sds9VQPwVJIJbs0v6oztb/view?usp=sharing), and put it to `gim/weights`
+
+3. Generate the articulated **cabinet** from the input images in `examples/cabinet`
+
+   ```bash
+   python run_two_parts.py
+   ```
+
+​	Then check `outputs/cabinet/sds_output/part_meshes` and  `outputs/cabinet/sds_output/states` for the articulated mesh results!
+
+​	You can also check `outputs/cabinet/output.urdf` for the **URDF** format output, and we provide a script to parse it in `pipelines/urdf.py`
+
+4. Try other examples by simply changing the input directory and the joint type.
+
+   ```bash
+   python run_two_parts.py --input_dir examples/cabinet2 --joint_type revolute
+   python run_two_parts.py --input_dir examples/cabinet3
+   ```
+   
+
+
+
+
+## 💻 PartNet-Mobility Articulation
+
+1. Download the **preprocessed PartNet-Mobility dataset** from [Google Drive](https://drive.google.com/file/d/1MGvp_Sm7lkn87ZXH3I1uSGNTJ_-hD4eA/view?usp=drive_link) (including **144** objects in **12** categories, the objects have been rendered with disks) and put it to the `datasets` directory. The folder structure should be 
+
+   ```bash
+   FreeArt3D
+   |--datasets
+       |-- PartNet
+           |-- 100214 
+           └-- ...
+   ```
+
+2. Then run the same script with specified input directory and the PartNet option
+
+   ```bash
+   python run_two_parts.py --input_dir datasets/PartNet/100214 --partnet # You can use any other object id included in the dataset 
+   ```
+   
+3. *(Optional)* Evaluate the metric of results
+
+   We provide the script to evaluate different metrics for the PartNet-Mobility results. Note that this is a general evaluation script so you can use it to evaluate other objects if you have ground-truth for them (simply changing the **test_id**)
+
+   ```bash
+   python evaluate_partnet.py --test_id 100214
+   ```
+
+4. *(Optional)* Preprocess the PartNet-Mobility dataset
+
+   If you want to test our method on the objects **not in** our processed dataset, you can use the following scripts the process meshes from the [original PartNet-Mobility dataset](https://sapien.ucsd.edu/downloads). Note that you need to manually filter out the joints you want to use. You can check `configs/partnet_target.json` and  `partnet.json` for the format. Here we provide the steps to **preprocess** and **render** the objects according to the object ids in `partnet.json` 
+
+   ```bash
+   python preprocess_partnet_joint.py
+   blenderproc run preprocess_partnet_mesh.py
+   python render_partnet_mesh.py
+   ```
+   
+   The scripts will generate **.glb** meshes at different qpos in `datasets/PartNet/{$test_id}/gt_mesh` and the renderings **with** **disk** in `datasets/PartNet/{$test_id}`, which is the same as the ones in the downloaded preprocessed dataset.
+
+5. *(Optional)* Download the PartNet-Mobility results
+
+   We provide the [PartNet-Mobility results](https://drive.google.com/drive/folders/1cthxRlejeTbU3AJC0OmRBeBx0pQr6gef?usp=sharing) run at our end with our latest version of the code for reference. The results are reproducible with the code, but you might get results with minor difference (we have fixed the random seed but some CUDA implementations (e.g. [tiny-cuda-nn](https://github.com/NVlabs/tiny-cuda-nn)) are non-deterministic)
+
+   
+
+## 🚪 Multi-joint Articulation
+
+1. Download the **processed multi-joint dataset** from [Google Drive](https://drive.google.com/file/d/1-Bl5X5XahyMzY9NvrMqsVUzCYthzU11w/view?usp=drive_link) (the objects have been rendered with disks for **each joint**) and put it to the `datasets`directory. The folder structure should be 
+
+   ```bash
+   FreeArt3D
+   |--datasets
+       |-- multi-joint
+           |-- 46180
+           |-- ...
+           └-- meta.json
+   ```
+
+   **Note:** This small dataset includes objects from both **PartNet-Mobility** and **Objaverse**. A meta file records the types of joints for each object.
+
+2. Run the script for multi-joint articulation
+
+   ```bash
+   python run_multi_parts.py --input_dir datasets/multi_joint/46180 # You can use any other object id included in the dataset
+   ```
+
+
+
+## 🌟 Use your own images 
+
+If you want to use your own images, you need to segment them first using tools such as SAM. Here is a [webtool](https://huggingface.co/spaces/Xenova/segment-anything-webgpu) you can refer to
+
+<img src="assets\sam.png" alt="sam" style="zoom: 33%;" />
+
+After segmentation, if your image wasn't segmented out with a **"carpet"**, you can manually add a **virtual disk** with a GUI tool we provided
+
+```bash
+python add_disk.py --input_dir examples/cabinet3_no_disk
+```
+
+This tool can help you add a virtual disk for every input image efficiently. It also supports rescaling the objects to a suitable size for better articulation.
+
+
+
+
+## 🤗 Demo
+We have deployed our live demo at [huggingface space](https://huggingface.co/spaces/MorPhLingXD/FreeArt3D). However, due to the long running time of our method, it's also recommended running the demo on your local machine:
+
+```bash
+python app.py
+```
+
+
+
+## 🍀 Acknowledgement
+
+We acknowledge the following repositories for borrowing the codes:
+
+**TRELLIS:** https://github.com/microsoft/TRELLIS
+
+**GIM:** https://github.com/xuelunshen/gim 
+
+
+
+## 📜 Citation	
+
+If you find this repository useful in your project, welcome to cite our work :)
+
+```
+[BibTex]
+@InProceedings{chen2025freeart3d,
+  title = {FreeArt3D: Training-Free Articulated Object Generation using 3D Diffusion},
+  author = {Chen, Chuhao and Liu, Isabella and Wei, Xinyue and Su, Hao and Liu, Minghua},
+  booktitle = {SIGGRAPH Asia 2025 Conference Papers},
+  year = {2025}
+}
+```

TRELLIS/trellis/pipelines/trellis_image_to_3d.py

@@ -346,10 +346,7 @@ class TrellisImageTo3DPipeline(Pipeline):
         
         # Decode occupancy latent
         decoder = self.models['sparse_structure_decoder']
-        decoded = decoder(z_s)
-        
-        # visualize_dense_voxels(noise[0].permute(1, 2, 3, 0)[..., :3], 16, 'noise_low')
-        # visualize_dense_voxels(z_s[0].permute(1, 2, 3, 0)[..., :3], 16, 'denoised_low')      
+        decoded = decoder(z_s) 
         coords = torch.argwhere(decoded > 0)[:, [0, 2, 3, 4]].int()
         return coords
 

TRELLIS/trellis/utils/postprocessing_utils.py

@@ -318,22 +318,30 @@ def bake_texture(
         texture = torch.zeros((texture_size * texture_size, 3), dtype=torch.float32).cuda()
         texture_weights = torch.zeros((texture_size * texture_size), dtype=torch.float32).cuda()
         rastctx = utils3d.torch.RastContext(backend='cuda')
+        
+        i = 0
         for observation, view, projection in tqdm(zip(observations, views, projections), total=len(observations), disable=not verbose, desc='Texture baking (fast)'):
-            with torch.no_grad():
+             
+            with torch.no_grad(): 
                 rast = utils3d.torch.rasterize_triangle_faces(
                     rastctx, vertices[None], faces, observation.shape[1], observation.shape[0], uv=uvs[None], view=view, projection=projection
                 )
                 uv_map = rast['uv'][0].detach().flip(0)
                 mask = rast['mask'][0].detach().bool() & masks[0]
             
-            # nearest neighbor interpolation
+            # nearest neighbor interpolation 
             uv_map = (uv_map * texture_size).floor().long()
             obs = observation[mask]
             uv_map = uv_map[mask]
             idx = uv_map[:, 0] + (texture_size - uv_map[:, 1] - 1) * texture_size
+
+            # Fix a bug that some indices are out of range
+            idx_mask = idx < texture_size * texture_size
+            idx = idx[idx_mask]
+            obs = obs[idx_mask]
             texture = texture.scatter_add(0, idx.view(-1, 1).expand(-1, 3), obs)
             texture_weights = texture_weights.scatter_add(0, idx, torch.ones((obs.shape[0]), dtype=torch.float32, device=texture.device))
-
+    
         mask = texture_weights > 0
         texture[mask] /= texture_weights[mask][:, None]
         texture = np.clip(texture.reshape(texture_size, texture_size, 3).cpu().numpy() * 255, 0, 255).astype(np.uint8)
@@ -404,6 +412,7 @@ def to_glb(
     fill_holes_max_size: float = 0.04,
     texture_size: int = 1024,
     debug: bool = False,
+    baking_mode: Literal['fast', 'opt'] = 'opt',
     verbose: bool = True,
 ) -> trimesh.Trimesh:
     """
@@ -443,15 +452,15 @@ def to_glb(
     observations, extrinsics, intrinsics = render_multiview(app_rep, resolution=1024, nviews=100)
     masks = [np.any(observation > 0, axis=-1) for observation in observations]
     extrinsics = [extrinsics[i].cpu().numpy() for i in range(len(extrinsics))]
-    intrinsics = [intrinsics[i].cpu().numpy() for i in range(len(intrinsics))]
+    intrinsics = [intrinsics[i].cpu().numpy() for i in range(len(intrinsics))] 
     texture = bake_texture(
         vertices, faces, uvs,
         observations, masks, extrinsics, intrinsics,
-        texture_size=texture_size, mode='opt',
+        texture_size=texture_size, mode=baking_mode,
         lambda_tv=0.01,
         verbose=verbose
     )
-    texture = Image.fromarray(texture)
+    texture = Image.fromarray(texture) 
 
     # rotate mesh (from z-up to y-up)
     vertices = vertices @ np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]])

add_disk.py

@@ -212,13 +212,13 @@ class EditorApp:
                 print(f"Failed: {fg_path} -> {e}", file=sys.stderr)
                 fail += 1
 
-        messagebox.showinfo("Batch Done", f"Saved {ok} image(s) to {output_dir}. {fail} failed.")
+        messagebox.showinfo("Batch Done", f"Saved {ok} image(s) to {output_dir}")
 
 def main():
 
     parser = argparse.ArgumentParser()
     parser.add_argument('--disk_path', type=str, default='assets/disk.png')
-    parser.add_argument('--input_dir', type=str, default='examples/cabinet4_no_disk')
+    parser.add_argument('--input_dir', type=str, default='examples/cabinet3_no_disk')
     args = parser.parse_args()
 
     bg_path = args.disk_path

app.py

@@ -1,89 +1,65 @@
 import os 
 import shutil
-import json
-import numpy as np 
-import subprocess
+import json 
+import numpy as np  
 import gradio as gr
 import spaces
-
-from huggingface_hub import snapshot_download
+import time
+  
 from gradio_litmodel3d import LitModel3D
+from pipelines.render import run_rendering
+from pipelines.recon import run_recon 
+from pipelines.estimate import run_estimate
+from pipelines.sds import run_sds
 from typing import * 
 from omegaconf import OmegaConf
 
-LOCAL_RUN = True
-if not LOCAL_RUN: 
-    subprocess.run(["bash", "setup.sh"], check=True)
-
 partnet_dir = f'datasets/PartNet'
 real_world_dir = f'examples'
 multi_joint_dir = f'datasets/multi_joint'
-
+ 
 with open(f"configs/partnet.json") as f:
     data_info = json.load(f) 
-cfg = OmegaConf.load('configs/default.yaml')
-
-# Ensure datasets/PartNet is available when running on Spaces (or locally if missing)
-def _ensure_partnet_dataset():
-    
-    if os.path.isdir(partnet_dir) and len(os.listdir(partnet_dir)) > 0:
-        return 
-    HF_TOKEN = os.getenv("HF_TOKEN")
-    snapshot_download(
-        repo_id="MorPhLingXD/FreeArt3D",
-        repo_type="dataset",
-        local_dir="datasets", 
-        token=HF_TOKEN,
-    ) 
-
-_ensure_partnet_dataset()
+cfg = OmegaConf.load('configs/default.yaml') 
 
 labels = {
     'Cabinet (real-world)': 'cabinet',
     'Cabinet2 (real-world)': 'cabinet2',
-    'Box (100214)': '100214',
+    'Box (100247)': '100247',
     'Dishwasher (12614)': '12614',
-    'Laptop (11405)': '11405',
-    'Lighter (100350)': '100350',
-    'Microwave (7263)': '7263',
-    'Oven (102044)': '102044',
-    'Refrigerator (10655)': '10655',
-    'Safe (101363)': '101363',
-    'Stapler (103299)': '103299',
+    'Laptop (10270)': '10270',
+    'Lighter (100309)': '100309',
+    'Microwave (7320)': '7320',
+    'Oven (102001)': '102001',
+    'Refrigerator (11231)': '11231',
+    'Safe (102301)': '102301',
+    'Stapler (103111)': '103111',
     'StorageFurniture (47183)': '47183',
-    'Table (22301)': '22301',
-    'WashingMachine (103775)': '103775',
+    'Table (20411)': '20411',
+    'WashingMachine (100283)': '100283',
 }
-
-MAX_SEED = np.iinfo(np.int32).max
+ 
 TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
 os.makedirs(TMP_DIR, exist_ok=True)
-
-def _clear_3d():
-    return (gr.update(value=None), gr.update(value=None), gr.update(value=None))
-
-def start_session(req: gr.Request):
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    os.makedirs(user_dir, exist_ok=True)
+ 
+def start_session(req: gr.Request): 
+    return req.session_hash
     
 def end_session(req: gr.Request):
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    shutil.rmtree(user_dir, ignore_errors=True)
+    pass
 
 def get_available_ids():
     """Get list of available object IDs for the dropdown"""
-    # real_world_ids = ['cabinet', 'cabinet2', 'cabinet3']
-    # # multi_joint_ids = [filename for filename in os.listdir(multi_joint_dir) if \
-    # #     os.path.isdir(os.path.join(multi_joint_dir, filename))]
-    # partnet_mobility_ids = list(data_info['total_obj_ids'])
-    # # total_list = real_world_ids + multi_joint_ids + partnet_mobility_ids
-    # total_list = real_world_ids + partnet_mobility_ids # TODO: Support multi-joints
+    # TODO: Support multi-joints
     total_list = list(labels.keys())
     return total_list
 
-def load_renderings_with_id(id_input: str) -> Tuple[List[str], str]:
+def load_renderings_with_id(id_input: str, session_hash: str) -> Tuple[List[str], str]:
     
     selected_id = labels[id_input]
+    print(f'[Selected Object] {selected_id}')
+    cfg.train_num_state = 6
+
     rendering_paths = []
     if 'cabinet' in selected_id:
         base_dir = real_world_dir
@@ -91,6 +67,7 @@ def load_renderings_with_id(id_input: str) -> Tuple[List[str], str]:
         base_dir = partnet_dir
     else:
         base_dir = multi_joint_dir
+
     for i in range(cfg.train_num_state):
         rendering_paths.append(f'{base_dir}/{selected_id}/{i:02d}_seg.png') 
     return rendering_paths, selected_id 
@@ -100,6 +77,7 @@ def handle_mesh_upload(files):
     if not files or len(files) == 0:
         return gr.Dropdown(choices=[], value=None), None
     choices = [f"Mesh State {i:02d} - {os.path.basename(file.name)}" for i, file in enumerate(files)]
+    # yield gr.update(), gr.update(None)
     return gr.Dropdown(choices=choices, value=choices[0] if choices else None), files[0].name
 
 def switch_mesh_view(selected_file, files):
@@ -124,66 +102,118 @@ def switch_mesh_view(selected_file, files):
     
     return None
  
-def process_and_update_gallery(mesh_files, mesh_source_flag, current_gallery):
+def process_and_update_gallery(mesh_files, session_hash):
     mesh_paths = [f.name for f in mesh_files] if mesh_files else []
     cfg.train_num_state = len(mesh_paths)
-    from pipelines.render import run_rendering
-    rendering_paths = run_rendering(mesh_paths, output_dir=TMP_DIR)
-    # rendering_paths is assumed to be a list of image file paths
-    if not rendering_paths:
-        return current_gallery or []
-    if current_gallery:
-        return current_gallery + rendering_paths
+    rendering_paths = run_rendering(mesh_paths, output_dir=f'{TMP_DIR}/{session_hash}') 
     return rendering_paths 
 
-def handle_image_gallery_upload(gallery_value, req: gr.Request):
-    """Normalize directly uploaded images in the Gallery by saving them to a per-session folder
-    and returning entries that point to stable file paths compatible with downstream processing.
-    """
-    try:
-        session_dir = os.path.join(TMP_DIR, str(req.session_hash), 'uploaded_images')
-    except Exception:
-        session_dir = os.path.join(TMP_DIR, 'uploaded_images')
-    os.makedirs(session_dir, exist_ok=True)
-
-    normalized_items = []
-    if not gallery_value:
-        return []
-
-    for idx, item in enumerate(gallery_value):
-        candidate = item
-        if isinstance(item, (list, tuple)) and len(item) > 0:
-            candidate = item[0]
-
-        saved_path = None
-        if isinstance(candidate, str) and os.path.exists(candidate):
-            # Copy to per-session folder for stability and cache isolation
-            base = os.path.basename(candidate)
-            stem, ext = os.path.splitext(base)
-            dest = os.path.join(session_dir, f"{stem}_{idx:02d}{ext or '.png'}")
+def format_meta_info_text(info_dict):
+    """Format joint meta info into 4 rich-text lines (larger font; bold title for Joint Axis)."""
+    def to_str(v):
+        if isinstance(v, np.ndarray):
+            v = v.tolist()
+        if isinstance(v, (list, dict, tuple)):
             try:
-                if os.path.abspath(candidate) != os.path.abspath(dest):
-                    shutil.copy(candidate, dest)
-                else:
-                    dest = candidate
-                saved_path = dest
+                return json.dumps(v)
             except Exception:
-                saved_path = candidate
-        else:
-            # Unrecognized type; keep as-is
-            saved_path = None
-
-        if saved_path is not None:
-            normalized_items.append([saved_path, None])
-        else:
-            normalized_items.append(item)
+                return str(v)
+        return "" if v is None else str(v)
 
-    return normalized_items
+    axis_val = info_dict.get("joint_axis", "")
+    pos_val = info_dict.get("joint_position", "")
+    scale_val = info_dict.get("joint_scale", "")
+    qpos_val = info_dict.get("joint_qpos", "")
+
+    axis_str = to_str(axis_val)
+    pos_str = to_str(pos_val)
+    scale_str = to_str(-scale_val)
+    qpos_str = to_str(qpos_val)
+
+    # Use inline HTML for size and emphasis; rendered by gr.Markdown
+    return (
+        "<div style=\"font-size: 18px; line-height: 1.5;\">"
+        f"<div><strong>Joint Axis:</strong> {axis_str}</div>"
+        f"<div><strong>Joint Pivot:</strong> {pos_str}</div>"
+        f"<div><strong>Max Scale:</strong> {scale_str}</div>"
+        f"<div><strong>qpos:</strong> {qpos_str}</div>"
+        "</div>"
+    )
 
-def save_gallery_to_renderings(gallery_value, selected_id):
+def image_gallery_state_change(p_state): 
+    base_dir = f'outputs/{p_state.get("sel_id")}_{p_state.get("session_hash")}' 
+    if p_state.get("step") == "running": 
+        while True:
+            with open(f'{base_dir}/state_list.json', 'r') as f:
+                state_list = json.load(f)
+            if state_list[0] == "done":
+                break
+            time.sleep(0.5)
+        return gr.update(), gr.update(), \
+             gr.update(), gr.update(), \
+             gr.update(), gr.update(), \
+             gr.update(), gr.update(), gr.update(), \
+             gr.update(), gr.update(), gr.update()
+    if p_state.get("step") == "done": 
+        return p_state.get("gallery", gr.update()), p_state.get("sel_id", gr.update()), \
+            p_state.get("cached_path", gr.update())[0],  \
+            p_state.get("cached_path", gr.update())[1],  \
+            p_state.get("cached_path", gr.update())[2],  \
+            gr.update(value=None), gr.update(value=None), \
+            gr.update(value=None), gr.update(value=None), \
+            gr.update(value=None), gr.update(value=None), gr.update(value=None) 
+    return gr.update(), gr.update(), \
+        gr.update(), gr.update(), \
+        gr.update(), gr.update(), \
+        gr.update(), gr.update(), gr.update(), \
+        gr.update(), gr.update(), gr.update()
+
+def recon_state_change(p_state): 
+    base_dir = f'outputs/{p_state.get("sel_id")}_{p_state.get("session_hash")}' 
+    if p_state.get("step") == "running": 
+        while True:
+            with open(f'{base_dir}/state_list.json', 'r') as f:
+                state_list = json.load(f) 
+            if state_list[1] == "done":
+                break
+            time.sleep(0.5)
+        return gr.update(), gr.update()
+    if p_state.get("step") == "done":
+        return p_state.get("vox", gr.update()), p_state.get("recon", gr.update())   
+    return gr.update(), gr.update()
+ 
+def estimate_state_change(p_state): 
+    base_dir = f'outputs/{p_state.get("sel_id")}_{p_state.get("session_hash")}' 
+    if p_state.get("step") == "running": 
+        while True:
+            with open(f'{base_dir}/state_list.json', 'r') as f:
+                state_list = json.load(f)
+            if state_list[2] == "done":
+                break
+            time.sleep(0.5)
+        return gr.update(), gr.update()
+    if p_state.get("step") == "done":
+        return p_state.get("match", gr.update()), p_state.get("html", gr.update())
+    return gr.update(), gr.update()
+
+def sds_state_change(p_state): 
+    base_dir = f'outputs/{p_state.get("sel_id")}_{p_state.get("session_hash")}' 
+    if p_state.get("step") == "running": 
+        while True:
+            with open(f'{base_dir}/state_list.json', 'r') as f:
+                state_list = json.load(f)
+            if state_list[3] == "done":
+                break
+            time.sleep(0.5)
+        return gr.update(), gr.update(), gr.update()
+    if p_state.get("step") == "done":
+        return p_state.get("full", gr.update()), p_state.get("fixed", gr.update()), p_state.get("art", gr.update())
+    return gr.update(), gr.update(), gr.update()
+
+def save_gallery_to_renderings(gallery_value, selected_id, session_hash):
     """Persist gallery images into outputs/<selected_id>/renderings with SDS-expected names.
     Ensures there are cfg.train_num_state frames by padding with the last image if needed.
-    Also writes the required 'renderings_pure_joint_00_state_{T-1}.png' using the last frame.
+    Also writes the required 'rendering_pure_joint_00_state_{T-1}.png' using the last frame.
     """
     try:
         image_paths = []
@@ -196,160 +226,50 @@ def save_gallery_to_renderings(gallery_value, selected_id):
             return
 
         cfg.train_num_state = len(image_paths)
-        base_dir = f'outputs/{selected_id}'
+        base_dir = f'outputs/{selected_id}_{session_hash}' 
         if "uploaded" in base_dir:
             os.system(f"rm -rf {base_dir}/*")
+            
         rendering_dir = f'{base_dir}/renderings'
         os.makedirs(rendering_dir, exist_ok=True)
 
         # Normalize count to T frames
         for i in range(cfg.train_num_state):
             src = image_paths[i] if i < len(image_paths) else image_paths[-1]
-            dst = f'{rendering_dir}/renderings_joint_00_state_{i:02d}.png'
+            dst = f'{rendering_dir}/rendering_joint_00_state_{i:02d}.png'
             shutil.copy(src, dst)
 
         # Write the 'pure' image as the last uploaded (or last padded) frame
-        pure_src = image_paths[-1]
-        pure_dst = f'{rendering_dir}/renderings_pure_joint_00_state_{(cfg.train_num_state - 1):02d}.png'
+        if os.path.exists(image_paths[-1].replace('_seg.png', '_pure.png')):
+            pure_src = image_paths[-1].replace('_seg.png', '_pure.png')
+        else:
+            pure_src = image_paths[-1]
+        pure_dst = f'{rendering_dir}/rendering_pure_joint_00_state_{(cfg.train_num_state - 1):02d}.png'
         shutil.copy(pure_src, pure_dst)
     except Exception as e:
         print(f"[Gallery->Renderings] Failed to persist images: {e}")
         return
 
-@spaces.GPU(duration=300)
-def recon_trellis_meshes(input_image_gallery, selected_id): 
+@spaces.GPU(duration=200)
+def run_recon_trellis(image_paths, output_dir): 
+    return run_recon(image_paths, output_dir=output_dir, app=True)
+
+def recon_trellis_meshes(input_image_gallery, output_dir): 
     # Carefully extract file paths from Gradio Gallery elements, which may be dicts, file objects, or strings.
     image_paths = [img[0] for img in input_image_gallery]  
-    from pipelines.recon import run_recon 
-    recon_voxel_paths, recon_mesh_paths = run_recon(image_paths, output_dir=f"outputs/{selected_id}/recon")
-    return recon_voxel_paths, recon_mesh_paths
-
-@spaces.GPU(duration=180)
-def estimate_initial_joints(input_image_gallery, selected_id, joint_type_user):
-    image_paths = [img[0] for img in input_image_gallery] 
-    if isinstance(joint_type_user, str) and joint_type_user in ("revolute", "prismatic"):
-        joint_type = joint_type_user
-    else:
-        joint_type = 'prismatic' if (selected_id in data_info['prismatic']['obj_ids']) else 'revolute'
-    from pipelines.estimate import run_estimate
-    matching_examples, info_dict = run_estimate(image_paths, output_dir=f"outputs/{selected_id}", 
-        joint_type=joint_type) 
+    glb_paths, rendering_dir, recon_voxel_paths = run_recon_trellis(image_paths, output_dir=output_dir)
+    recon_mesh_paths = run_rendering(glb_paths, rendering_dir, recon=True)
+    return recon_voxel_paths, recon_mesh_paths 
+
+@spaces.GPU(duration=120)
+def estimate_initial_joints(input_image_gallery, joint_type, output_dir):
+    image_paths = [img[0] for img in input_image_gallery]
+    print(image_paths)
+    matching_examples, info_dict = run_estimate(image_paths, output_dir=output_dir, cfg=cfg, joint_type=joint_type) 
     return matching_examples, info_dict
 
-def meta_to_fields_ui(info_dict):
-    """Convert meta info dict to UI field values (textboxes x/y/z, slider)."""
-    def to_str(v):
-        if isinstance(v, np.ndarray):
-            v = v.tolist()
-        if isinstance(v, (list, dict, tuple)):
-            try:
-                return json.dumps(v)
-            except Exception:
-                return str(v)
-        return "" if v is None else str(v)
- 
-    # Extract axis components
-    axis_val = info_dict.get("joint_axis", "")
-    if isinstance(axis_val, np.ndarray):
-        axis_val = axis_val.tolist()
-    if isinstance(axis_val, (list, tuple)) and len(axis_val) >= 3:
-        ax_x, ax_y, ax_z = axis_val[0], axis_val[1], axis_val[2]
-    else:
-        ax_x = ax_y = ax_z = ""
-    ax_x_str = to_str(ax_x)
-    ax_y_str = to_str(ax_y)
-    ax_z_str = to_str(ax_z)
-
-    # Extract position components
-    pos_val = info_dict.get("joint_position", "")
-    if isinstance(pos_val, np.ndarray):
-        pos_val = pos_val.tolist()
-    if isinstance(pos_val, (list, tuple)) and len(pos_val) >= 3:
-        px, py, pz = pos_val[0], pos_val[1], pos_val[2]
-    else:
-        px = py = pz = ""
-    px_str = to_str(px)
-    py_str = to_str(py)
-    pz_str = to_str(pz)
-
-    # Extract scalar scale value for slider
-    scale_val = -info_dict.get("joint_scale", 1.0) 
-    qpos_str = to_str(info_dict.get("joint_qpos", ""))
-
-    return ( 
-        ax_x_str,
-        ax_y_str,
-        ax_z_str,
-        px_str,
-        py_str,
-        pz_str,
-        scale_val,
-        qpos_str,
-    )
-
-def fields_to_meta_ui(
-    axis_x: str,
-    axis_y: str,
-    axis_z: str,
-    pos_x: str,
-    pos_y: str,
-    pos_z: str,
-    scale_value: float,
-    joint_qpos: str,
-):
-    """Convert new UI fields (x/y/z textboxes, slider) to meta info dict."""
-    def parse_float(text: Any):
-        try:
-            return float(text)
-        except Exception:
-            return None
-
-    def parse_val(text: str):
-        if text is None:
-            return ""
-        t = str(text).strip()
-        if t == "":
-            return ""
-        try:
-            return json.loads(t)
-        except Exception:
-            if "," in t:
-                parts = [p.strip() for p in t.split(",")]
-                try:
-                    return [float(p) for p in parts]
-                except Exception:
-                    return parts
-            try:
-                return float(t)
-            except Exception:
-                return t
-
-    axis_vals = [parse_float(axis_x), parse_float(axis_y), parse_float(axis_z)]
-    if all(v is not None for v in axis_vals):
-        joint_axis = axis_vals
-    else:
-        joint_axis = ""
-
-    pos_vals = [parse_float(pos_x), parse_float(pos_y), parse_float(pos_z)]
-    if all(v is not None for v in pos_vals):
-        joint_position = pos_vals
-    else:
-        joint_position = ""
-
-    try:
-        joint_scale = -float(scale_value)
-    except Exception:
-        joint_scale = -1.0
-        
-    return {
-        "joint_axis": joint_axis,
-        "joint_position": joint_position,
-        "joint_scale": joint_scale,
-        "joint_qpos": parse_val(joint_qpos),
-    }
-
-@spaces.GPU(duration=600)
-def articulated_generation_sds(selected_id):
+@spaces.GPU(duration=800)
+def articulated_generation_sds(selected_id, session_hash):
 
     if 'cabinet' in selected_id:
         input_dir = real_world_dir
@@ -358,19 +278,122 @@ def articulated_generation_sds(selected_id):
     else:
         input_dir = multi_joint_dir
 
-    base_dir = f'outputs/{selected_id}'
+    base_dir = f'outputs/{selected_id}_{session_hash}' 
     rendering_dir = f'{base_dir}/renderings'
 
     os.makedirs(rendering_dir, exist_ok=True)
     if os.path.exists(f'{input_dir}/{selected_id}/05_seg.png'):
         for i in range(6):
-            shutil.copy(f'{input_dir}/{selected_id}/{i:02d}_seg.png', f'{rendering_dir}/renderings_joint_00_state_{i:02d}.png')
-        shutil.copy(f'{input_dir}/{selected_id}/05_pure.png', f'{rendering_dir}/renderings_pure_joint_00_state_05.png')
-    
-    from pipelines.sds import run_sds
+            shutil.copy(f'{input_dir}/{selected_id}/{i:02d}_seg.png', f'{rendering_dir}/rendering_joint_00_state_{i:02d}.png')
+        shutil.copy(f'{input_dir}/{selected_id}/05_pure.png', f'{rendering_dir}/rendering_pure_joint_00_state_05.png')
+  
     full_mesh, fixed_part, articulated_part = run_sds(base_dir, 1, cfg)
     return full_mesh, fixed_part, articulated_part
 
+def pipeline(id_value, mesh_files, uploaded_image_gallery, joint_type_in, session_hash, run_idx):
+    """Server-side pipeline that yields state only; UI updates are applied via a mapper."""
+ 
+    # 1) Prepare inputs  
+    if id_value is not None:
+        sel_id = f"{labels[id_value]}_{run_idx:02d}"
+    elif mesh_files is not None:
+        sel_id = f"mesh_uploading_{run_idx:02d}"
+    elif uploaded_image_gallery is not None:
+        sel_id = f"image_uploading_{run_idx:02d}" 
+
+    base_dir = f'outputs/{sel_id}_{session_hash}' 
+    os.makedirs(base_dir, exist_ok=True)
+    state_list_output_path = f'{base_dir}/state_list.json'
+
+    state_list = ["running", "prepare", "prepare", "prepare"]
+    with open(state_list_output_path, 'w') as f:
+        json.dump(state_list, f)
+    yield (
+        {"step": state_list[0], "sel_id": sel_id, "session_hash": session_hash},
+        {"step": state_list[1], "sel_id": sel_id, "session_hash": session_hash},
+        {"step": state_list[2], "sel_id": sel_id, "session_hash": session_hash},
+        {"step": state_list[3], "sel_id": sel_id, "session_hash": session_hash}, run_idx, \
+        f"<h3>Preparing input images...<p style=\"color:red\">(<u>~3 min</u>)</p></h3>"
+    )
+
+    if id_value is not None:
+        rendering_paths, _ = load_renderings_with_id(id_value, session_hash)
+    elif mesh_files is not None:
+        rendering_paths = process_and_update_gallery(mesh_files, session_hash) 
+    elif uploaded_image_gallery is not None:
+        rendering_paths = [img[0] for img in uploaded_image_gallery]
+
+    gallery = [[p, None] for p in rendering_paths] 
+    cached_path = [f'datasets/cached_results/{labels[id_value]}/full.glb',
+        f'datasets/cached_results/{labels[id_value]}/fixed.glb',
+        f'datasets/cached_results/{labels[id_value]}/art.glb']
+    save_gallery_to_renderings(gallery, sel_id, session_hash)
+
+    state_list = ["done", "running", "prepare", "prepare"]
+    with open(state_list_output_path, 'w') as f:
+        json.dump(state_list, f)
+    yield (
+        {"step": state_list[0], "gallery": gallery, "sel_id": sel_id, "cached_path": cached_path, "session_hash": session_hash},
+        {"step": state_list[1], "sel_id": sel_id, "session_hash": session_hash},
+        {"step": state_list[2], "sel_id": sel_id, "session_hash": session_hash},
+        {"step": state_list[3], "sel_id": sel_id, "session_hash": session_hash}, run_idx, \
+        f"<h3>Stage 1: Initial reconstruction... <span style=\"color:red\">(<u style=\"color:red\">~3 min</u>)</span></h3>"
+    )
+ 
+    # 3) Reconstruction
+    vox, recon = recon_trellis_meshes(gallery, f"{base_dir}/recon")
+
+    state_list = ["done", "done", "running", "running"]
+    with open(state_list_output_path, 'w') as f:
+        json.dump(state_list, f)
+    yield (
+        {"step": state_list[0], "gallery": gallery, "sel_id": sel_id, "cached_path": cached_path, "session_hash": session_hash},
+        {"step": state_list[1], "vox": vox, "recon": recon, "sel_id": sel_id, "session_hash": session_hash},
+        {"step": state_list[2], "sel_id": sel_id, "session_hash": session_hash},
+        {"step": state_list[3], "sel_id": sel_id, "session_hash": session_hash}, run_idx, \
+        f"<h3>Stage 2: Joint initialization... <span style=\"color:red\">(<u style=\"color:red\">~1 min</u>)</span></h3>"
+    )
+
+    # 4) Joint estimation
+    if joint_type_in is not None:
+        joint_type = joint_type_in
+    else:
+        if 'cabinet' in sel_id:
+            joint_type = 'revolute' if 'cabinet2' in sel_id else 'prismatic'
+        else:
+            joint_type = 'prismatic' if (sel_id in data_info['prismatic']['obj_ids']) else 'revolute'
+    print(f"[Joint Type] {joint_type}")
+    match, meta = estimate_initial_joints(gallery, joint_type, base_dir)
+    
+    # 5) Format meta info
+    html = format_meta_info_text(meta)
+
+    state_list = ["done", "done", "done", "running"]
+    with open(state_list_output_path, 'w') as f:
+        json.dump(state_list, f)
+    yield (
+        {"step": state_list[0], "gallery": gallery, "sel_id": sel_id, "cached_path": cached_path, "session_hash": session_hash},
+        {"step": state_list[1], "vox": vox, "recon": recon, "sel_id": sel_id, "session_hash": session_hash},
+        {"step": state_list[2], "match": match, "html": html, "sel_id": sel_id, "session_hash": session_hash},
+        {"step": state_list[3], "sel_id": sel_id, "session_hash": session_hash}, run_idx, \
+        f"<h3>Stage 3: SDS optimization... <span style=\"color:red\">(<u style=\"color:red\">~10 min</u>)</span></h3>"
+    )
+
+    # 6) SDS refinement 
+    full, fixed, art = articulated_generation_sds(sel_id, session_hash)
+    
+    state_list = ["done", "done", "done", "done"]
+    with open(state_list_output_path, 'w') as f:
+        json.dump(state_list, f)
+    yield (
+        {"step": state_list[0], "gallery": gallery, "sel_id": sel_id, "cached_path": cached_path,"session_hash": session_hash},
+        {"step": state_list[1], "vox": vox, "recon": recon, "sel_id": sel_id, "session_hash": session_hash},
+        {"step": state_list[2], "match": match, "html": html, "sel_id": sel_id, "session_hash": session_hash},
+        {"step": state_list[3], "full": full, "fixed": fixed, "art": art, "sel_id": sel_id, "session_hash": session_hash},
+        run_idx+1, \
+        f"<h3>Done!</h3>"
+    )
+
 with gr.Blocks(delete_cache=(600, 600), css="""
     .gallery-container {
         overflow-x: auto !important;
@@ -416,22 +439,25 @@ with gr.Blocks(delete_cache=(600, 600), css="""
     gr.Markdown("""
     ## FreeArt3D Demo
     ### You can use the following three methods to generate articulated 3D models.
-    1. Choose a PartNet-Mobility object ID and click "Submit ObjectID" to load the pre-rendered input images.
-    2. Upload multiple mesh files (e.g., .obj, .ply, .stl, .glb, .gltf) and render the input images with the disk.
-    3. Upload multiple input images directly.
+    1. Choose an example object (from PartNet-Mobility or real-world).
+    2. Upload multiple mesh files (e.g., .obj, .ply, .stl, .glb, .gltf) at different articulation states (images will be rendered automatically).
+    3. Upload multiple input images with **a segmented object on the disk** at **different articulation states** (you can use a fixed view but have to contain ** at least 2 states**).
+    
+    ### The total generation time is *~10 min*. Be patient :)
+    ### Currently it's difficult to run the full pipeline with ZeroGPU due to the long running time. We are applying for an exclusive GPU. Stay Tuned! (We add the cached results now for example objects for you to have a quick check)
     """)
 
     with gr.Row(equal_height=True): 
-        with gr.Tab("Load by Object ID"):
+        with gr.Tab("Load by Example Object"):
             with gr.Row():
                 id_input = gr.Dropdown(
                     choices=get_available_ids(),
-                    value='cabinet',
-                    label="Object ID",
-                    info='Select a real-world object ID or an object ID from the PartNet-Mobility dataset. (e.g., Box (100214))',
+                    value='Cabinet (real-world)',
+                    label="Example Object",
+                    info='Select a real-world object or an object from the PartNet-Mobility dataset. (e.g., Box (100214))',
                 )
             with gr.Row():
-                id_input_btn = gr.Button("Submit Object ID", variant="primary") 
+                id_input_btn = gr.Button("Generate Articulated Object", variant="primary") 
         with gr.Tab("Upload Mesh Files"):
             with gr.Row(equal_height=True): 
                 with gr.Column():
@@ -440,25 +466,32 @@ with gr.Blocks(delete_cache=(600, 600), css="""
                 with gr.Column():
                     mesh_viewer = LitModel3D(label="Selected Mesh", exposure=10.0, height=300)
             with gr.Row():
-                mesh_processing_btn = gr.Button("Process Meshes", variant="secondary")
+                joint_type_in_mesh = gr.Dropdown(label="joint_type", choices=["revolute", "prismatic"], value=None)
+            with gr.Row():
+                mesh_input_btn = gr.Button("Generate Articulated Object", variant="primary") 
+        with gr.Tab("Upload Image Files"):
+            with gr.Row(equal_height=True):
+                uploaded_image_gallery = gr.Gallery(label="Input Images", show_label=True, elem_id="gallery", height=300, columns=10, allow_preview=True, elem_classes=["gallery-container"])
+            with gr.Row():
+                joint_type_in_image = gr.Dropdown(label="joint_type", choices=["revolute", "prismatic"], value=None)
+            with gr.Row():
+                image_input_btn = gr.Button("Generate Articulated Object", variant="primary") 
+
+    with gr.Row():  
+        with gr.Accordion("Current Stage", open=True):
+            current_stage = gr.HTML(value="<h3>Waiting for input...</h3>")
+
 
     with gr.Row(equal_height=True):
         with gr.Column(): 
-            input_image_gallery = gr.Gallery(label="Input Images", show_label=True, elem_id="gallery", height=300, columns=3, allow_preview=True, elem_classes=["gallery-container"])
-            input_image_confirm_btn = gr.Button("Confirm Images", variant="secondary")
+            input_image_gallery = gr.Gallery(label="Input Images", show_label=True, elem_id="gallery", height=300, columns=3, allow_preview=True, interactive=False, elem_classes=["gallery-container"])
         with gr.Column():
-            voxel_gallery = gr.Gallery(label="Reconstructed Voxels", show_label=True, elem_id="gallery", height=360, interactive=False, columns=3, allow_preview=True, elem_classes=["gallery-container"])
+            voxel_gallery = gr.Gallery(label="Reconstructed Voxels", show_label=True, elem_id="gallery", height=360, columns=3, allow_preview=True, interactive=False, elem_classes=["gallery-container"])
         with gr.Column():
-            recon_mesh_gallery = gr.Gallery(label="Reconstructed Meshes", show_label=True, elem_id="gallery", height=360, interactive=False, columns=3, allow_preview=True, elem_classes=["gallery-container"])
-
-    with gr.Row():
-        joint_type_in = gr.Dropdown(label="joint_type", choices=["revolute", "prismatic"], value=None)
-
-    with gr.Row():
-        estimate_btn = gr.Button("Estimate Initial Joints", variant="secondary")
-
+            recon_mesh_gallery = gr.Gallery(label="Reconstructed Meshes", show_label=True, elem_id="gallery", height=360, columns=3, allow_preview=True, interactive=False, elem_classes=["gallery-container"])
+ 
     with gr.Row():
-        matching_examples = gr.Gallery(label="Matching Examples", show_label=True, elem_id="gallery", height=400, interactive=False, columns=5, allow_preview=True, elem_classes=["gallery-container"])
+        matching_examples = gr.Gallery(label="Correspondences", show_label=True, elem_id="gallery", height=400, interactive=False, columns=5, allow_preview=True, elem_classes=["gallery-container"])
     
     with gr.Row(): 
         meta_info = gr.State(
@@ -470,54 +503,98 @@ with gr.Blocks(delete_cache=(600, 600), css="""
             }
         )
         with gr.Accordion("Joint Information", open=True):
-            with gr.Row():
-                axis_x_in = gr.Textbox(label="Joint Axis X", placeholder="", interactive=False)
-                axis_y_in = gr.Textbox(label="Joint Axis Y", placeholder="", interactive=False)
-                axis_z_in = gr.Textbox(label="Joint Axis Z", placeholder="", interactive=False)
-            with gr.Row():
-                pos_x_in = gr.Textbox(label="Joint Pivot X", placeholder="", interactive=False)
-                pos_y_in = gr.Textbox(label="Joint Pivot Y", placeholder="", interactive=False)
-                pos_z_in = gr.Textbox(label="Joint Pivot Z", placeholder="", interactive=False)
-            joint_scale_in = gr.Slider(label="Max Scale", minimum=0.0, maximum=5.0, step=0.01, value=1.0, interactive=False)
-            joint_qpos_in = gr.Textbox(label="qpos", placeholder="", interactive=False)
-    
-    with gr.Row():
-        sds_btn = gr.Button("Generate Articulated Object", variant="primary")
-
-    with gr.Row():
+            joint_info_html = gr.HTML(value="")
+     
+    with gr.Row(): 
         with gr.Column():
             full_mesh = LitModel3D(label="Full Mesh", exposure=10.0, height=300)
         with gr.Column():
             fixed_part = LitModel3D(label="Fixed Part", exposure=10.0, height=300)
         with gr.Column():
             articulated_part = LitModel3D(label="Articulated Part", exposure=10.0, height=300)
+    with gr.Row():
+        with gr.Column():
+            full_mesh_cached = LitModel3D(label="Full Mesh (Cached)", exposure=10.0, height=300)
+        with gr.Column():
+            fixed_part_cached = LitModel3D(label="Fixed Part (Cached)", exposure=10.0, height=300)
+        with gr.Column():
+            articulated_part_cached = LitModel3D(label="Articulated Part (Cached)", exposure=10.0, height=300)
 
-    mesh_source_flag = gr.State(value="uploaded")  # default to uploaded
-    selected_id_state = gr.State(value="uploaded")
+    selected_id_state = gr.State(value="")
+    session_hash_state = gr.State(value="")
+    run_idx_state = gr.State(value=0)
+    
+    image_gallery_state = gr.State(value="prepare")
+    recon_state = gr.State(value="prepare")
+    estimate_state = gr.State(value="prepare")
+    sds_state = gr.State(value="prepare")
+    none_val = gr.State(value=None)
 
     # Handlers
-    demo.load(start_session)
+    demo.load(start_session, outputs=session_hash_state)
     demo.unload(end_session)
- 
+
     id_input_btn.click(
-        load_renderings_with_id,
-        inputs=id_input,
-        outputs=[input_image_gallery, selected_id_state]
+        start_session,
+        outputs=session_hash_state,
+    ).then(
+        pipeline,
+        inputs=[id_input, none_val, none_val, none_val, session_hash_state, run_idx_state],
+        outputs=[image_gallery_state, recon_state, estimate_state, sds_state, run_idx_state, current_stage],
+    )
+
+    mesh_input_btn.click(
+        start_session,
+        outputs=session_hash_state,
+    ).then(
+        pipeline,
+        inputs=[none_val, mesh_files, none_val, joint_type_in_mesh, session_hash_state, run_idx_state],
+        outputs=[image_gallery_state, recon_state, estimate_state, sds_state, run_idx_state, current_stage],
+    )
+
+    image_input_btn.click(
+        start_session,
+        outputs=session_hash_state,
+    ).then(
+        pipeline,
+        inputs=[none_val, none_val, uploaded_image_gallery, joint_type_in_image, session_hash_state, run_idx_state],
+        outputs=[image_gallery_state, recon_state, estimate_state, sds_state, run_idx_state, current_stage],
+    )
+
+    # Watch state and update only relevant components per step
+    image_gallery_state.change(
+        image_gallery_state_change,
+        inputs=image_gallery_state,
+        outputs=[input_image_gallery, selected_id_state, full_mesh_cached, fixed_part_cached, articulated_part_cached,
+            voxel_gallery, recon_mesh_gallery,
+            matching_examples, joint_info_html,
+            full_mesh, fixed_part, articulated_part],
+    )
+
+    recon_state.change(
+        recon_state_change,
+        inputs=recon_state,
+        outputs=[voxel_gallery, recon_mesh_gallery],
+    ) 
+
+    estimate_state.change(
+        estimate_state_change,
+        inputs=estimate_state,
+        outputs=[matching_examples, joint_info_html],
     )
 
+    sds_state.change(
+        sds_state_change,
+        inputs=sds_state,
+        outputs=[full_mesh, fixed_part, articulated_part],
+    )
+    
     # Handle mesh file uploads
     mesh_files.change(
         handle_mesh_upload,
         inputs=mesh_files,
         outputs=[mesh_selector, mesh_viewer]
     )
-    
-    # Set flag to 'uploaded' when uploading files
-    mesh_files.change(
-        lambda *_: "uploaded",
-        inputs=None,
-        outputs=mesh_source_flag
-    )
 
     # Handle dropdown selection for mesh view
     mesh_selector.change(
@@ -526,75 +603,12 @@ with gr.Blocks(delete_cache=(600, 600), css="""
         outputs=mesh_viewer
     )
 
-    # Handle Process button click
-    mesh_processing_btn.click(
-        process_and_update_gallery,
-        inputs=[mesh_files, mesh_source_flag, input_image_gallery],
-        outputs=input_image_gallery
-    )
-
     # Ensure downstream uses the generic 'uploaded' namespace for output paths
-    input_image_gallery.change(
+    uploaded_image_gallery.change(
         lambda *_: "uploaded",
         inputs=None,
         outputs=selected_id_state
     )
-
-    input_image_confirm_btn.click(
-        save_gallery_to_renderings,
-        inputs=[input_image_gallery, selected_id_state],
-        outputs=None
-    ).then(
-        recon_trellis_meshes,
-        inputs=[input_image_gallery, selected_id_state],
-        outputs=[voxel_gallery, recon_mesh_gallery]
-    )
-
-    estimate_btn.click(
-        estimate_initial_joints,
-        inputs=[input_image_gallery, selected_id_state, joint_type_in],
-        outputs=[matching_examples, meta_info]
-    ).then(
-        meta_to_fields_ui,
-        inputs=meta_info,
-        outputs=[ 
-            axis_x_in,
-            axis_y_in,
-            axis_z_in,
-            pos_x_in,
-            pos_y_in,
-            pos_z_in,
-            joint_scale_in,
-            joint_qpos_in
-        ]
-    ).then(
-        lambda x: x,
-        inputs=meta_info,
-        outputs=meta_info
-    )
-
-    sds_btn.click(
-        fields_to_meta_ui,
-        inputs=[
-            axis_x_in,
-            axis_y_in,
-            axis_z_in,
-            pos_x_in,
-            pos_y_in,
-            pos_z_in,
-            joint_scale_in,
-            joint_qpos_in,
-        ],
-        outputs=meta_info
-    ).then(
-        _clear_3d,                  
-        inputs=None,
-        outputs=[full_mesh, fixed_part, articulated_part]
-    ).then(
-        articulated_generation_sds,
-        inputs=selected_id_state,
-        outputs=[full_mesh, fixed_part, articulated_part]
-    )
  
 # Launch the Gradio app
 if __name__ == "__main__":   

configs/default.yaml

@@ -1,16 +1,33 @@
-total_iters: 3000
+total_iters: 1500
 print_interval: 10
 save_interval: 500
 vis_interval: 500
 
+min_azi: 0.39269908169872414
+max_azi: 1.1780972450961724
+ 
+ransac_iteration: 1000
+ransac_threshold: 0.1
+ransac_sample: 3
+
 noise_start: 0.5
 noise_end: 0.8
 refine_noise: 0.5
+expand_iteration: 100
+expand_threshold_base: 0.5
+expand_threshold_prismatic: 0.45
+expand_threshold_revolute: 0.45
+large_iteration_base: 50
+large_iteration_prismatic: 30
+large_iteration_revolute: 30
 
 batch_size: 1
 base_lr: 0.01
 art_lr: 0.01
-scale_lr: 0.01
+pivot_lr: 0.1
+scale_lr_prismatic: 0.005
+scale_lr_revolute: 0.01
+qpos_lr: 0.01
 
 sds_weight: 0.1
 voxel_weight: 1
@@ -20,4 +37,4 @@ sample_interval: 0.5
 sample_coeff: 2.0
 
 train_num_state: 6
-inference_num_state: 6 
+inference_num_state: 6

configs/partnet_target.json

@@ -64,7 +64,7 @@
         "joint_type": "revolute"
     },
     "Refrigerator": {
-        "joint_tags": ["door"],
+        "joint_tags": ["door", "door_frame"],
         "joint_type": "revolute"
     },
     "WashingMachine": {

datasets/PartNet/100214/gt_mesh/00.glb

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:aa33bf488300581d45bedaa1d1286e0a240068d1433b33d4b3625b95ff8b5e20
-size 3908732

datasets/PartNet/100214/gt_mesh/01.glb

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:9ba38020066067c5c9948892387d2cde48dec9c3d9aeaa4fe489b1851b6aad5c
-size 3908756

datasets/PartNet/100214/gt_mesh/02.glb

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3adecc17b8dfe4f5fbd1da9695b9d634fa8c85f01ad472f56774224a63e74387
-size 3908756

datasets/PartNet/100214/gt_mesh/03.glb

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:716fb1a554ca852c3e850fffe2f43db1fe908a152140c8409937bdb6b419a19a
-size 3908756

datasets/PartNet/100214/gt_mesh/04.glb

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:0039fc82c8b7904f30e77cbcef101d31555960fc0eed0955fa625b7842000995
-size 3908840

datasets/PartNet/100214/gt_mesh/05.glb

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8a6976d0390034206ebe897bb682140556f1e83712540c714f01e20a9c74c875
-size 3908728

datasets/PartNet/100214/joint_info.json

@@ -1,21 +0,0 @@
-[
-    {
-        "type": "revolute",
-        "range": [
-            -1.6755160819145563,
-            0.3490658503988659
-        ],
-        "axis": {
-            "origin": [
-                0.4566900000000005,
-                0.19353231481404498,
-                -0.22
-            ],
-            "direction": [
-                -1.0,
-                0.0,
-                0.0
-            ]
-        }
-    }
-]

datasets/PartNet/100214/joints.json

@@ -1,16 +0,0 @@
-{
-    "category": "Box",
-    "joints": [
-        "joint_0"
-    ],
-    "descendants": {
-        "joint_0": [],
-        "joint_1": [],
-        "joint_2": [],
-        "joint_3": [
-            "joint_0",
-            "joint_1",
-            "joint_2"
-        ]
-    }
-}

datasets/PartNet/100214/mobility.urdf

@@ -1,470 +0,0 @@
-<?xml version="1.0" ?>
-<robot name="partnet_802f6cfeb6f41f16a59ff8378a27445f">
-	<link name="base"/>
-	<link name="link_0">
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-4.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-9.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-37.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-36.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-34.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-35.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-38.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-12.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-13.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-15.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-16.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-18.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-19.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-21.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-20.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-17.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-14.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-32.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-31.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-30.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-29.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-26.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-27.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-25.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-24.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-23.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-33.obj"/>
-			</geometry>
-		</visual>
-		<visual name="rotation_lid-1">
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-28.obj"/>
-			</geometry>
-		</visual>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-4.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-9.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-37.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-36.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-34.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-35.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-38.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-12.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-13.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-15.obj"/>
-			</geometry>
-		</collision>
-		<collision>
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-		<collision>
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-		</collision>
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-			<geometry>
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-			</geometry>
-		</collision>
-		<collision>
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-			<geometry>
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-			</geometry>
-		</collision>
-		<collision>
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-			</geometry>
-		</collision>
-		<collision>
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-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-31.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-30.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-29.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
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-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-27.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-25.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-24.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-23.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-33.obj"/>
-			</geometry>
-		</collision>
-		<collision>
-			<origin xyz="-0.4566900000000005 -0.19353231481404498 0.22"/>
-			<geometry>
-				<mesh filename="textured_objs/original-28.obj"/>
-			</geometry>
-		</collision>
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-	<joint name="joint_0" type="revolute">
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datasets/PartNet/100350/joints.json

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-{
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