MagicArticulate：让您的3D模型具备关节动作准备能力

MagicArticulate: Make Your 3D Models Articulation-Ready

February 17, 2025

作者: Chaoyue Song, Jianfeng Zhang, Xiu Li, Fan Yang, Yiwen Chen, Zhongcong Xu, Jun Hao Liew, Xiaoyang Guo, Fayao Liu, Jiashi Feng, Guosheng Lin

cs.AI

摘要

随着3D内容创作的爆炸性增长，对将静态3D模型自动转换为支持逼真动画的可关节版本的需求不断增加。传统方法主要依赖手动注释，这既耗时又劳动密集。此外，缺乏大规模基准数据集阻碍了基于学习的解决方案的发展。在这项工作中，我们提出了MagicArticulate，这是一个有效的框架，可以自动将静态3D模型转换为可关节的资产。我们的主要贡献有三个方面。首先，我们引入了Articulation-XL，这是一个大规模基准数据集，包含超过33k个高质量关节标注的3D模型，经过精心筛选自Objaverse-XL。其次，我们提出了一种新颖的骨骼生成方法，将任务构建为一个序列建模问题，利用自回归变换器自然处理骨骼中不同数量的骨头或关节以及它们在不同3D模型中的固有依赖关系。第三，我们使用功能扩散过程预测蒙皮权重，该过程结合了顶点和关节之间的体积测地距离先验。大量实验证明，MagicArticulate在各种物体类别上明显优于现有方法，实现了高质量的关节标注，从而实现了逼真的动画。项目页面：https://chaoyuesong.github.io/MagicArticulate。

English

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.