AniGen: Unified S³ Fields for Animatable 3D Asset Generation

AniGen is a unified framework that directly generates animate-ready 3D assets conditioned on a single image. Our key insight is to represent shape, skeleton, and skinning as mutually consistent $S^3$ Fields (Shape, Skeleton, Skin) defined over a shared spatial domain. To enable the robust learning of these fields, we introduce two technical innovations: (i) a confidence-decaying skeleton field that explicitly handles the geometric ambiguity of bone prediction at Voronoi boundaries, and (ii) a dual skin feature field that decouples skinning weights from specific joint counts, allowing a fixed-architecture network to predict rigs of arbitrary complexity. Built upon a two-stage flow-matching pipeline, AniGen first synthesizes a sparse structural scaffold and then generates dense geometry and articulation in a structured latent space. Extensive experiments demonstrate that AniGen substantially outperforms state-of-the-art sequential baselines in rig validity and animation quality, generalizing effectively to in-the-wild images across diverse categories including animals, humanoids, and machinery.

🔮 Overview

AniGen takes a single image as input and automatically produces a fully rigged, animate-ready 3D asset, complete with a coherent mesh, an articulated skeleton, and smooth skinning weights. The generated assets can be directly imported into standard 3D pipelines and driven by off-the-shelf motion data, enabling immediate deployment across a wide spectrum of downstream applications, including embodied AI agent construction, physics-based simulation, character animation, dynamic scene creation, and articulated object manipulation.

Machine Arm	Machine Dog	Money Tree	Iron Boy
Mairo	Evo	Horse	Eagle

📦 Installation

Prerequisites

• System: The code is currently tested only on Linux.
• Hardware: An NVIDIA GPU with at least 18GB of memory is necessary. The code has been verified on NVIDIA A800 and RTX3090 GPUs.
• Software:
  • The CUDA Toolkit is needed to compile certain submodules. The code has been tested with CUDA versions 11.8 and 12.2.
  • Conda is recommended for managing dependencies.
  • Python version 3.8 or higher is required.

Installation Steps

1. Clone the repo:

   git clone --recurse-submodules https://github.com/VAST-AI-Research/AniGen.git
   cd AniGen

2. Install the dependencies:

   We recommend using uv for fast, reliable installs. The setup script will also work with plain pip if uv is not available.

   Create a new virtual environment and install everything:

   source ./setup.sh --new-env --all

   If your network connection to PyPI is unstable or slow, you can use the Tsinghua mirror:

   source ./setup.sh --new-env --all --tsinghua

   If you already have an environment with PyTorch installed, install into it directly:

   source ./setup.sh --basic

   [!NOTE] The setup script auto-detects your CUDA version and installs matching wheels for PyTorch, spconv, pytorch3d, and nvdiffrast. DSINE (used for surface normal estimation) is loaded at runtime via torch.hub and does not require separate installation.

🤖 Pretrained Models

We provide the following pretrained models on Hugging Face. Please make sure to download all necessary weights from this page, including the required dinov2, dsine, and vgg checkpoints.

Tip

Recommended: Use SS-Flow-Duet + SLAT-Flow-Auto if you do not have specific requirements.

• For more detailed skeleton (including character fingers) → SS-Flow-Duet
• For better geometry generalization → SS-Flow-Solo
• SLAT-Flow-Control supports density levels 0–4, but if the density condition significantly deviates from the proper value for the object, skinning weights may be damaged.

DAE Model	Description	Download
SS-DAE	Encoder&Decoder of SS	Download
SLAT-DAE	Encoder&Decoder of SLAT	Download

SS Model	Description	Download
SS-Flow-Duet	Detailed Skeleton (Full-FT Geo)	Download
SS-Flow-Epic	Geometry&Skeleton Balanced (LoRA-FT Geo)	Download
SS-Flow-Solo	Accurate Geometry (Freeze Geo)	Download

SLAT Model	Description	Download
SLAT-Flow-Auto	Automatically Determine Joint Number	Download
SLAT-Flow-Control	Controllable Joint Density	Download

💡 Usage

Minimal Example

Here is an example of how to use the pretrained models for 3D asset generation.

After running the code, you will get the following files:

• mesh.glb: a rigged mesh file
• skeleton.glb: a skeleton visualization file
• processed_image.png: the masked image as the condition

AniGen Pipeline (Rigged Mesh + Skeleton)

For AniGen checkpoints in this repo (e.g. ckpts/anigen/ss_flow_solo + ckpts/anigen/slat_flow_control), you can run:

python example.py --image_path assets/cond_images/trex.png

Web Demo

app.py provides a simple web demo for 3D asset generation. Since this demo is based on Gradio, additional dependencies are required:

source ./setup.sh --demo

If needed, you can also install the demo dependencies via the Tsinghua mirror:

source ./setup.sh --demo --tsinghua

After installing the dependencies, you can run the demo with the following command:

python app.py

Then, you can access the demo at the address shown in the terminal.

The web demo is also available on Hugging Face Spaces!

🏋️ Training

Training Data

Sample training data is available at AniGen-Sample-Dataset. To prepare your own data, refer to TRELLIS and the sample data format.

Prerequisites

Note

Training requires the CUBVH extension (extensions/CUBVH/), which is automatically built by setup.sh. It is not needed for inference (app.py, example.py).

Training Commands

The pipeline has five stages. Later stages depend on earlier ones, so please train in order:

# Stage 1: Skin AutoEncoder
python train.py --config configs/anigen_skin_ae.json --output_dir outputs/anigen_skin_ae

# Stage 2: Sparse Structure DAE
python train.py --config configs/ss_dae.json --output_dir outputs/ss_dae

# Stage 3: Structured Latent DAE
python train.py --config configs/slat_dae.json --output_dir outputs/slat_dae

# Stage 4: SS Flow Matching (image-conditioned generation)
python train.py --config configs/ss_flow_duet.json --output_dir outputs/ss_flow_duet

# Stage 5: SLAT Flow Matching (image-conditioned generation)
python train.py --config configs/slat_flow_auto.json --output_dir outputs/slat_flow_auto

Multi-Node / Multi-GPU

Append the following flags for distributed training across multiple machines and GPUs:

python train.py --config configs/<config>.json --output_dir outputs/<output> \
    --num_nodes XX --node_rank XX --master_addr XX --master_port XX

Model Variants

Other SS Flow variants (ss_flow_epic, ss_flow_solo) and SLAT Flow variants (slat_flow_control, slat_flow_gsn_auto) are available under ckpts/anigen/. Their config files can be found at ckpts/anigen/<variant>/config.json.

Resume / Restart

Training automatically resumes from the latest checkpoint in --output_dir. To start fresh, pass --ckpt none.

License

This project's source code is released under the MIT License.

Important

This repository includes third-party components with additional license restrictions. In particular, extensions/CUBVH/ contains BVH code derived from NVIDIA's instant-ngp, which is licensed for non-commercial / research use only. See THIRD_PARTY_LICENSES.md for details.

Acknowledgements

• TRELLIS by Microsoft
• cuBVH by Jiaxiang Tang
• tiny-cuda-nn and instant-ngp by Thomas Müller / NVIDIA
• FlexiCubes by NVIDIA

We sincerely appreciate the contributions of these excellent projects and their authors. We believe open source helps accelerate research, lower barriers to innovation, and make progress more accessible to the broader community.

📜 Citation

If you find this work helpful, please consider citing our paper:

@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}
}