MagicArticulate: Make Your 3D Models Articulation-Ready
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915157061402624 |
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| author | Song, Chaoyue Zhang, Jianfeng Li, Xiu Yang, Fan Chen, Yiwen Xu, Zhongcong Liew, Jun Hao Guo, Xiaoyang Liu, Fayao Feng, Jiashi Lin, Guosheng |
| author_facet | Song, Chaoyue Zhang, Jianfeng Li, Xiu Yang, Fan Chen, Yiwen Xu, Zhongcong Liew, Jun Hao Guo, Xiaoyang Liu, Fayao Feng, Jiashi Lin, Guosheng |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_12135 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MagicArticulate: Make Your 3D Models Articulation-Ready Song, Chaoyue Zhang, Jianfeng Li, Xiu Yang, Fan Chen, Yiwen Xu, Zhongcong Liew, Jun Hao Guo, Xiaoyang Liu, Fayao Feng, Jiashi Lin, Guosheng Computer Vision and Pattern Recognition Graphics 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. |
| title | MagicArticulate: Make Your 3D Models Articulation-Ready |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2502.12135 |