UniRig: One Model to Rig Them All

This repository contains the official implementation for the SIGGRAPH'25 (TOG) UniRig framework, a unified solution for automatic 3D model rigging, developed by Tsinghua University and Tripo.

Paper: One Model to Rig Them All: Diverse Skeleton Rigging with UniRig

Overview

Rigging 3D models – creating a skeleton and assigning skinning weights – is a crucial but often complex and time-consuming step in 3D animation. UniRig tackles this challenge by introducing a novel, unified framework leveraging large autoregressive models to automate the process for a diverse range of 3D assets.

Combining UniRig with keyframe animation produces these following results:

The full UniRig system consists of two main stages:

1. Skeleton Prediction: An GPT-like transformer autoregressively predicts a topologically valid skeleton hierarchy using a novel Skeleton Tree Tokenization scheme.
2. Skinning Weight & Attribute Prediction: A Bone-Point Cross Attention mechanism predicts per-vertex skinning weights and relevant bone attributes (e.g., for physics simulation) based on the predicted skeleton and input mesh geometry.

This repository provides the code implementation for the entire framework vision, with components being released progressively.

Key Features (Full UniRig Framework)

• Unified Model: Aims to handle diverse model categories (humans, animals, objects) with a single framework.
• Automated Skeleton Generation: Predicts topologically valid skeleton structures. (✅ Available in current release)
• Automated Skinning Prediction: Predicts per-vertex skinning weights. (✅ Available in current release)
• Bone Attribute Prediction: Predicts attributes like stiffness for physics-based secondary motion. (⏳ Coming Soon)
• High Accuracy & Robustness: Achieves state-of-the-art results on challenging datasets (as shown in the paper with Rig-XL/VRoid training).
• Efficient Tokenization: Uses Skeleton Tree Tokenization for compact representation and efficient processing.
• Human-in-the-Loop Ready: Designed to potentially support iterative refinement workflows.

🚨 Current Release Status & Roadmap 🚨

We are open-sourcing UniRig progressively. Please note the current status:

Available Now (Initial Release):

• ✅ Code: Implementation for skeleton and skinning prediction.
• ✅ Model: Skeleton & Skinning Prediction checkpoint trained on Articulation-XL2.0. Available on Hugging Face.

Planned Future Releases:

• ⏳ Release of the Rig-XL and VRoid datasets used in the paper.
• ⏳ Full UniRig model checkpoints (Skeleton + Skinning) trained on Rig-XL/VRoid, replicating the paper's main results.

We appreciate your patience as we prepare these components for release. Follow VAST-AI-Research announcements for updates!

Installation

1. Prerequisites:

   • Python 3.11
   • PyTorch (tested with version >=2.3.1)

2. Clone the repository:

   git clone https://github.com/VAST-AI-Research/UniRig
   cd UniRig

3. Set up a virtual environment (recommended):

   conda create -n UniRig python=3.11
   conda activate UniRig

4. Install dependencies:

   python -m pip install torch torchvision
   python -m pip install -r requirements.txt
   python -m pip install spconv-{you-cuda-version}
   python -m pip install torch_scatter torch_cluster -f https://data.pyg.org/whl/torch-{your-torch-version}+{your-cuda-version}.html --no-cache-dir
   python -m pip install numpy==1.26.4

5. Download Model Checkpoint: The currently available skeleton prediction model checkpoint is hosted on Hugging Face and will typically be downloaded automatically by the provided scripts/functions.

6. (Optional, for importing/exporting .vrm) Install the blender addon: The blender addon is modifed from VRM-Addon-for-Blender.

   Make sure you are in the root directory of the project, then:

   python -c "import bpy, os; bpy.ops.preferences.addon_install(filepath=os.path.abspath('blender/add-on-vrm-v2.20.77_modified.zip'))"

Usage

Skeleton Prediction (Available Now)

Generate a skeleton for your 3D model using our pre-trained model. The process automatically analyzes the geometry and predicts an appropriate skeletal structure.

# Process a single file
bash launch/inference/generate_skeleton.sh --input examples/giraffe.glb --output results/giraffe_skeleton.fbx

# Process multiple files in a directory
bash launch/inference/generate_skeleton.sh --input_dir <your_input_directory> --output_dir <your_output_directory>

# Try different skeleton variations by changing the random seed
bash launch/inference/generate_skeleton.sh --input examples/giraffe.glb --output results/giraffe_skeleton.fbx --seed 42

Supported input formats: .obj, .fbx, .glb, and .vrm

Skinning Weight Prediction (Available Now)

# Skin a single file
bash launch/inference/generate_skin.sh --input examples/skeleton/giraffe.fbx --output results/giraffe_skin.fbx

# Process multiple files in a directory
bash launch/inference/generate_skin.sh --input_dir <your_input_directory> --output_dir <your_output_directory>

Note that the command above uses an edited-version from skeleton phase. The results may degrade significantly if the skeleton is inaccurate — for example, if tail bones or wing bones are missing. Therefore, it is recommended to refine the skeleton before performing skinning in order to achieve better results.

Merge the Predicted Results

Combine the predicted skeleton with your original 3D model to create a fully rigged asset:

# Merge skeleton from skeleton prediction
bash launch/inference/merge.sh --source results/giraffe_skeleton.fbx --target examples/giraffe.glb --output results/giraffe_rigged.glb

# Or merge skin from skin prediction
bash launch/inference/merge.sh --source results/giraffe_skin.fbx --target examples/giraffe.glb --output results/giraffe_rigged.glb

Models

Available models are hosted on the: https://huggingface.co/VAST-AI/UniRig

System Requirements

• CUDA-enabled GPU with at least 8GB VRAM

Citation

@article{zhang2025unirig,
  title={One Model to Rig Them All: Diverse Skeleton Rigging with UniRig},
  author={Zhang, Jia-Peng and Pu, Cheng-Feng and Guo, Meng-Hao and Cao, Yan-Pei and Hu, Shi-Min},
  journal={arXiv preprint arXiv:2504.12451},
  year={2025}
}

Acknowledgements

We would like to thank the following open-source projects and research works:

• OPT for model architecture
• 3DShape2VecSet for 3D shape representation
• SAMPart3D and Michelangelo for shape encoder implementation
• Articulation-XL2.0 for a curated dataset

We are grateful to the broader research community for their open exploration and contributions to the field of 3D generation.