[2605.09196] RigidFormer: Learning Rigid Dynamics using Transformers

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Computer Science > Computer Vision and Pattern Recognition

arXiv:2605.09196 (cs)

[Submitted on 9 May 2026]

Title:RigidFormer: Learning Rigid Dynamics using Transformers

Authors:Zhiyang Dou, Minghao Guo, Haixu Wu, Doug Roble, Tuur Stuyck, Wojciech Matusik<br>View a PDF of the paper titled RigidFormer: Learning Rigid Dynamics using Transformers, by Zhiyang Dou and 5 other authors

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Abstract:Learning-based simulation of multi-object rigid-body dynamics remains difficult because contact is discontinuous and errors compound over long horizons. Most existing methods remain tied to mesh connectivity and vertex-level message passing, which limits their applicability to mesh-free inputs such as point clouds and leads to high computational cost. Efficiently modeling high-fidelity rigid-body dynamics from mesh-free representations, therefore, remains challenging. We introduce RigidFormer, an object-centric Transformer-based model that learns mesh-free rigid-body dynamics with controllable integration step sizes. RigidFormer reasons at the object level and advances each object through compact anchors; Anchor-Vertex Pooling enriches these anchors with local vertex features, retaining contact-relevant geometry without dense vertex-level interaction. We propose Anchor-based RoPE to inject anchor geometry into attention while respecting the unordered nature of objects and anchors: object-token processing is permutation-equivariant, and the mean-pooled anchor descriptor is invariant to anchor reindexing while preserving shape extent. RigidFormer further enforces rigidity by projecting updates onto the rigid-body manifold using differentiable Kabsch alignment. On standard benchmarks, RigidFormer outperforms or matches mesh-based baselines using point inputs, runs faster, generalizes to unseen point resolutions and across datasets, and scales to 200+ objects; we also show a preliminary extension to command-conditioned articulated bodies by treating body parts as interacting object-level components.

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Subjects:

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG); Robotics (cs.RO)

Cite as:<br>arXiv:2605.09196 [cs.CV]

(or<br>arXiv:2605.09196v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2605.09196

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arXiv-issued DOI via DataCite (pending registration)

Submission history<br>From: Zhiyang Dou [view email]<br>[v1]<br>Sat, 9 May 2026 22:31:09 UTC (5,725 KB)

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