[2512.11798] Particulate: Feed-Forward 3D Object Articulation

Computer Science > Computer Vision and Pattern Recognition arXiv:2512.11798 (cs) [Submitted on 12 Dec 2025 (v1), last revised 27 Mar 2026 (this version, v2)] Title:Particulate: Feed-Forward 3D Object Articulation Authors:Ruining Li, Yuxin Yao, Chuanxia Zheng, Christian Rupprecht, Joan Lasenby, Shangzhe Wu, Andrea Vedaldi View a PDF of the paper titled Particulate: Feed-Forward 3D Object Articulation, by Ruining Li and 6 other authors View PDF HTML (experimental) 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 pr...