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README.md

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 license: mit
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-<p align="center"><a href="https://arxiv.org/pdf/2604.08746"><img src='https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white' alt='arXiv'></a>
-<a href='https://yihua7.github.io/AniGen-web/'><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/VAST-AI/AniGen'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Live_Demo-blue'></a>
-<a href='https://github.com/VAST-AI-Research/AniGen'><img src='https://img.shields.io/badge/GitHub-Repository-black?logo=github&logoColor=white' alt='GitHub'></a>
-</p>
+# AniGen_Weights
+
+Pretrained checkpoints for [AniGen](https://github.com/VAST-AI-Research/AniGen), a unified framework for generating animatable 3D assets from a single image.
+
+<p align="center">
+  <a href="https://arxiv.org/pdf/2604.08746"><img src="https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white" alt="arXiv"></a>
+  <a href="https://yihua7.github.io/AniGen-web/"><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/VAST-AI/AniGen"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Live_Demo-blue" alt="Hugging Face Demo"></a>
+  <a href="https://github.com/VAST-AI-Research/AniGen"><img src="https://img.shields.io/badge/GitHub-Repository-black?logo=github&logoColor=white" alt="GitHub"></a>
+</p>
+
+This repository stores the contents of the `ckpts/` directory used by the AniGen codebase, including:
+
+- AniGen stage checkpoints
+- DINOv2 vision encoder weights
+- DSINE normal estimation weights
+- VGG backbone weights
+
+## What Is Included
+
+The repository is organized exactly like the `ckpts/` folder expected by the main AniGen repo:
+
+```text
+ckpts/
+├── anigen/
+│   ├── ss_dae/
+│   ├── slat_dae/
+│   ├── ss_flow_duet/
+│   ├── ss_flow_epic/
+│   ├── ss_flow_solo/
+│   ├── slat_flow_auto/
+│   ├── slat_flow_control/
+│   └── slat_flow_gsn_auto/
+├── dinov2/
+├── dsine/
+└── vgg/
+```
+
+Approximate total size: about 23 GB.
+
+## Recommended Checkpoints
+
+For most users, we recommend:
+
+- `ss_flow_duet` for sparse structure generation
+- `slat_flow_auto` for structured latent generation
+
+This combination is also the default setup used by the AniGen inference example.
+
+## Checkpoint Overview
+
+### Core AniGen checkpoints
+
+| Folder | Purpose |
+| --- | --- |
+| `ckpts/anigen/ss_dae` | Sparse Structure autoencoder |
+| `ckpts/anigen/slat_dae` | Structured Latent autoencoder |
+| `ckpts/anigen/ss_flow_duet` | SS flow model with stronger skeleton detail |
+| `ckpts/anigen/ss_flow_epic` | SS flow model balancing geometry and skeleton quality |
+| `ckpts/anigen/ss_flow_solo` | SS flow model with stronger geometry generalization |
+| `ckpts/anigen/slat_flow_auto` | SLAT flow model with automatic joint-count prediction |
+| `ckpts/anigen/slat_flow_control` | SLAT flow model with controllable joint density |
+| `ckpts/anigen/slat_flow_gsn_auto` | Additional SLAT variant included in the release |
+
+### Dependency checkpoints
+
+| Folder | Purpose |
+| --- | --- |
+| `ckpts/dinov2` | DINOv2 encoder files and pretrained ViT-L/14 weights |
+| `ckpts/dsine` | DSINE normal estimation weights |
+| `ckpts/vgg` | VGG weights used by the pipeline |
+
+## How To Use
+
+Clone the main AniGen repository first:
+
+```bash
+git clone --recurse-submodules https://github.com/VAST-AI-Research/AniGen.git
+cd AniGen
+```
+
+Then download this weights repository so that the folder structure is preserved under the project root.
+
+### Option 1: Download with `huggingface_hub`
+
+```bash
+python -c "from huggingface_hub import snapshot_download; snapshot_download(repo_id='VAST-AI/AniGen_Weights', repo_type='model', local_dir='.', local_dir_use_symlinks=False)"
+```
+
+After download, you should have paths like:
+
+```text
+ckpts/anigen/ss_flow_duet/ckpts/denoiser.pt
+ckpts/anigen/slat_flow_auto/ckpts/denoiser.pt
+ckpts/dsine/dsine.pt
+ckpts/vgg/vgg16-397923af.pth
+```
+
+### Option 2: Download from the web UI
+
+You can also download this repository from the Hugging Face file browser and place the entire `ckpts/` folder at the root of the AniGen project.
+
+## Run AniGen With These Weights
+
+Once the `ckpts/` folder is in place, you can run:
+
+```bash
+python example.py --image_path assets/cond_images/trex.png
+```
+
+Or launch the Gradio demo:
+
+```bash
+python app.py
+```
+
+## Notes
+
+- Keep the directory names unchanged. The AniGen code expects the exact `ckpts/...` layout shown above.
+- The code repository may automatically fetch missing files in some setups, but this weights repository is the recommended way to pre-download and manage checkpoints explicitly.
+- `slat_flow_control` supports joint density control, while `slat_flow_auto` is the best default for general use.
+
+## Related Links
+
+- Best AI 3D studio -- Tripo: https://www.tripo3d.ai/zh
+- Main code repository: https://github.com/VAST-AI-Research/AniGen
+- Project page: https://yihua7.github.io/AniGen-web/
+- Demo: https://huggingface.co/spaces/VAST-AI/AniGen
+- Paper: https://arxiv.org/pdf/2604.08746
+
+## Citation
+
+```bibtex
+@article{huang2026anigen,
+  title     = {AniGen: Unified $S^3$ Fields for Animatable 3D Asset Generation},
+  author    = {Huang, Yi-Hua and Zhou, Zi-Xin and He, Yuting and Chang, Chirui
+               and Pu, Cheng-Feng and Yang, Ziyi and Guo, Yuan-Chen
+               and Cao, Yan-Pei and Qi, Xiaojuan},
+  journal   = {ACM SIGGRAPH},
+  year      = {2026}
+}
+```