MagicArticulate: Make Your 3D Models Articulation-Ready
Chaoyue Song, Jianfeng Zhang, Xiu Li, Fan Yang, Yiwen Chen, Zhongcong Xu, Jun Hao Liew, Xiaoyang Guo, Fayao Liu, Jiashi Feng, Guosheng Lin
2025-02-18
Summary
This paper talks about MagicArticulate, a new system that can automatically make 3D models ready for animation. It's like giving static 3D objects a digital skeleton so they can move naturally.
What's the problem?
Creating 3D models that can move realistically is usually a time-consuming process that requires a lot of manual work by skilled artists. There aren't many good tools to do this automatically, and there hasn't been enough data to train AI systems to do it well.
What's the solution?
The researchers created MagicArticulate, which does three main things. First, they made a huge collection of 3D models with movement information called Articulation-XL. Second, they developed a smart way to create digital skeletons for 3D models using AI. Third, they made a system to figure out how the 3D model's surface should move with the skeleton. All of this works together to automatically prepare 3D models for animation.
Why it matters?
This matters because it could make creating animated 3D content much faster and easier. It could help game developers, animators, and virtual reality creators make more realistic moving objects without spending so much time on the technical setup. This technology could lead to more diverse and interesting 3D animations in games, movies, and other digital media.
Abstract
With the explosive growth of 3D content creation, there is an increasing demand for automatically converting static 3D models into articulation-ready versions that support realistic animation. Traditional approaches rely heavily on manual annotation, which is both time-consuming and labor-intensive. Moreover, the lack of large-scale benchmarks has hindered the development of learning-based solutions. In this work, we present MagicArticulate, an effective framework that automatically transforms static 3D models into articulation-ready assets. Our key contributions are threefold. First, we introduce Articulation-XL, a large-scale benchmark containing over 33k 3D models with high-quality articulation annotations, carefully curated from Objaverse-XL. Second, we propose a novel skeleton generation method that formulates the task as a sequence modeling problem, leveraging an auto-regressive transformer to naturally handle varying numbers of bones or joints within skeletons and their inherent dependencies across different 3D models. Third, we predict skinning weights using a functional diffusion process that incorporates volumetric geodesic distance priors between vertices and joints. Extensive experiments demonstrate that MagicArticulate significantly outperforms existing methods across diverse object categories, achieving high-quality articulation that enables realistic animation. Project page: https://chaoyuesong.github.io/MagicArticulate.