UniRig

UniRig: One Model to Rig Them All

> [!IMPORTANT]
> ### 🔥 Exciting News: Introducing SkinTokens! 🔥
> 
> We are thrilled to announce SkinTokens, the powerful successor to UniRig (SIGGRAPH '25). 
> 
> While UniRig uses separate stages for skeleton prediction and skinning, SkinTokens unifies both into a single autoregressive sequence via learned discrete skin tokens. By introducing Reinforcement Learning and an Efficient Skinning Compression Module, it pushes the boundaries of automated 3D rigging even further:
> 
> - 🚀 98%–133% improvement in skinning accuracy!
> - 🦴 17%–22% improvement in bone prediction over state-of-the-art baselines!
> 
> 👉 Click here to explore the SkinTokens repository now!

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.
*   ✅ Dataset: Release of the Rig-XL and VRoid datasets used in the paper

……（后略，见原文）