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.

翻译：随着三维内容创作的爆炸式增长，将静态三维模型自动转换为支持逼真动画的、具备就绪关节结构版本的需求日益增长。传统方法严重依赖人工标注，既耗时又费力。此外，大规模基准数据的缺乏阻碍了基于学习的解决方案的发展。在本工作中，我们提出了MagicArticulate，这是一个将静态三维模型自动转换为具备就绪关节结构资产的有效框架。我们的主要贡献有三方面。首先，我们引入了Articulation-XL，这是一个包含超过33,000个带有高质量关节标注的三维模型的大规模基准数据集，这些数据是从Objaverse-XL中精心筛选整理的。其次，我们提出了一种新颖的骨架生成方法，将该任务构建为一个序列建模问题，利用自回归Transformer自然地处理骨架中不同数量的骨骼或关节，以及它们在不同三维模型间的固有依赖关系。第三，我们通过一个结合了顶点与关节间体积测地距离先验的函数扩散过程来预测蒙皮权重。大量实验表明，MagicArticulate在多种物体类别上显著优于现有方法，实现了能够支持逼真动画的高质量关节结构。项目页面：https://chaoyuesong.github.io/MagicArticulate。