Particulate: Feed-Forward 3D Object Articulation
We present Particulate, a feed-forward approach that, given a single static 3D mesh of an everyday object, directly infers all attributes of the underlying articulated structure, including its 3D parts, kinematic structure, and motion constraints. At its core is a transformer network, Part Articulation Transformer, which processes a point cloud of the input mesh using a flexible and scalable architecture to predict all the aforementioned attributes with native multi-joint support. We train the network end-to-end on a diverse collection of articulated 3D assets from public datasets. During inference, Particulate lifts the network's feed-forward prediction to the input mesh, yielding a fully articulated 3D model in seconds, much faster than prior approaches that require per-object optimization. Particulate can also accurately infer the articulated structure of AI-generated 3D assets, enabling full-fledged extraction of articulated 3D objects from a single (real or synthetic) image when combined with an off-the-shelf image-to-3D generator. 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. Quantitative and qualitative results show that Particulate significantly outperforms state-of-the-art approaches.
693fd046f516c69324681339
Ruining Li
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2025-12-16T22:33:00.811Z
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Ruining Li
rayli
user
rayli
693fd046f516c6932468133a
Yuxin Yao
693fd046f516c6932468133b
Chuanxia Zheng
693fd046f516c6932468133c
Christian Rupprecht
693fd046f516c6932468133d
Joan Lasenby
693fd046f516c6932468133e
Shangzhe Wu
693fd046f516c6932468133f
Andrea Vedaldi
Particulate: Feed-Forward 3D Object Articulation
We present Particulate, a feed-forward approach that, given a single static 3D mesh of an everyday object, directly infers all attributes of the underlying articulated structure, including its 3D parts, kinematic structure, and motion constraints. At its core is a transformer network, Part Articulation Transformer, which processes a point cloud of the input mesh using a flexible and scalable architecture to predict all the aforementioned attributes with native multi-joint support. We train the network end-to-end on a diverse collection of articulated 3D assets from public datasets. During inference, Particulate lifts the network's feed-forward prediction to the input mesh, yielding a fully articulated 3D model in seconds, much faster than prior approaches that require per-object optimization. Particulate can also accurately infer the articulated structure of AI-generated 3D assets, enabling full-fledged extraction of articulated 3D objects from a single (real or synthetic) image when combined with an off-the-shelf image-to-3D generator. 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. Quantitative and qualitative results show that Particulate significantly outperforms state-of-the-art approaches.
Particulate uses a transformer network to directly infer articulated 3D structure attributes from single static meshes with faster processing than prior optimization-based methods.
transformer network point cloud feed-forward approach 3D parts kinematic structure motion constraints Part Articulation Transformer end-to-end training articulated 3D model image-to-3D generator benchmark evaluation protocol