Particulate: Feed-Forward 3D Object Articulation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911547728592896 |
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| author | Li, Ruining Yao, Yuxin Zheng, Chuanxia Rupprecht, Christian Lasenby, Joan Wu, Shangzhe Vedaldi, Andrea |
| author_facet | Li, Ruining Yao, Yuxin Zheng, Chuanxia Rupprecht, Christian Lasenby, Joan Wu, Shangzhe Vedaldi, Andrea |
| contents | We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion constraints. The model is based on a transformer network, the Part Articulation Transformer, which predicts all these parameters for all joints. We train the network end-to-end on a diverse collection of articulated 3D assets from public datasets. During inference, Particulate maps the output of the network back to the input mesh, yielding a fully articulated 3D model in seconds, much faster than prior approaches that require per-object optimization. Particulate also works on AI-generated 3D assets, enabling the generation of articulated 3D objects from a single (real or synthetic) image when combined with an off-the-shelf image-to-3D model. We further introduce a new challenging benchmark for 3D articulation estimation curated from high-quality public 3D assets, and redesign the evaluation protocol to be more consistent with human preferences. Empirically, Particulate significantly outperforms state-of-the-art approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11798 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Particulate: Feed-Forward 3D Object Articulation Li, Ruining Yao, Yuxin Zheng, Chuanxia Rupprecht, Christian Lasenby, Joan Wu, Shangzhe Vedaldi, Andrea Computer Vision and Pattern Recognition Artificial Intelligence Graphics We introduce Particulate, a feed-forward model that, given a 3D mesh of an object, infers its articulations, including its 3D parts, their kinematic structure, and the motion constraints. The model is based on a transformer network, the Part Articulation Transformer, which predicts all these parameters for all joints. We train the network end-to-end on a diverse collection of articulated 3D assets from public datasets. During inference, Particulate maps the output of the network back to the input mesh, yielding a fully articulated 3D model in seconds, much faster than prior approaches that require per-object optimization. Particulate also works on AI-generated 3D assets, enabling the generation of articulated 3D objects from a single (real or synthetic) image when combined with an off-the-shelf image-to-3D model. We further introduce a new challenging benchmark for 3D articulation estimation curated from high-quality public 3D assets, and redesign the evaluation protocol to be more consistent with human preferences. Empirically, Particulate significantly outperforms state-of-the-art approaches. |
| title | Particulate: Feed-Forward 3D Object Articulation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Graphics |
| url | https://arxiv.org/abs/2512.11798 |