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:this https URL.
View on arXiv@article{song2025_2502.12135,
title={ MagicArticulate: Make Your 3D Models Articulation-Ready },
author={ Chaoyue Song and Jianfeng Zhang and Xiu Li and Fan Yang and Yiwen Chen and Zhongcong Xu and Jun Hao Liew and Xiaoyang Guo and Fayao Liu and Jiashi Feng and Guosheng Lin },
journal={arXiv preprint arXiv:2502.12135},
year={ 2025 }
}