{"ID":2829961,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.11798","arxiv_id":"2512.11798","title":"Particulate: Feed-Forward 3D Object Articulation","abstract":"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.","short_abstract":"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....","url_abs":"https://arxiv.org/abs/2512.11798","url_pdf":"https://arxiv.org/pdf/2512.11798v2","authors":"[\"Ruining Li\",\"Yuxin Yao\",\"Chuanxia Zheng\",\"Christian Rupprecht\",\"Joan Lasenby\",\"Shangzhe Wu\",\"Andrea Vedaldi\"]","published":"2025-12-12T18:59:51Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.GR\"]","methods":"[\"Transformer\"]","has_code":false}