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VAST-AI/
AniGen

microsoft/TRELLIS-image-large

finetune

mit

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AniGen_Weights

Pretrained checkpoints for AniGen, a unified framework for generating animatable 3D assets from a single image.

arXiv

Project Page

GitHub

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`

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:

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_control supports joint density control, while slat_flow_auto is the best default for general use.

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}
}

VAST-AI/AniGen