AniGen_Weights
Pretrained checkpoints for AniGen, a unified framework for generating animatable 3D assets from a single image.
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:
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_duetfor sparse structure generationslat_flow_autofor 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:
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
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:
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:
python example.py --image_path assets/cond_images/trex.png
Or launch the Gradio demo:
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_controlsupports joint density control, whileslat_flow_autois the best default for general use.
Related Links
- Best AI 3D studio -- Tripo: https://www.tripo3d.ai
- 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
@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}
}