PhysConvex: Physics-Informed 3D Dynamic Convex Radiance Fields for Reconstruction and Simulation

Dan Wang; Xinrui Cui; Serge Belongie; Ravi Ramamoorthi

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arXiv · 2602.18886

PhysConvex: Physics-Informed 3D Dynamic Convex Radiance Fields for Reconstruction and Simulation

Dan Wang·Xinrui Cui·Serge Belongie·Ravi Ramamoorthi

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Abstract

Reconstructing and simulating dynamic 3D scenes with both visual realism and physical consistency remains a fundamental challenge. Existing neural representations, such as NeRFs and 3DGS, excel in appearance reconstruction but struggle to capture complex material deformation and dynamics. We propose PhysConvex, a Physics-informed 3D Dynamic Convex Radiance Field that unifies visual rendering and physical simulation. PhysConvex represents deformable radiance fields using physically grounded convex primitives governed by continuum mechanics. We introduce a boundary-driven dynamic convex representation that models deformation through vertex and surface dynamics, capturing spatially adaptive, non-uniform deformation, and evolving boundaries. To efficiently simulate complex geometries and heterogeneous materials, we further develop a reduced-order convex simulation that advects dynamic convex fields using neural skinning eigenmodes as shape- and material-aware deformation bases with time-varying reduced DOFs under Newtonian dynamics. Convex dynamics also offers compact, gap-free volumetric coverage, enhancing both geometric efficiency and simulation fidelity. Experiments demonstrate that PhysConvex achieves high-fidelity reconstruction of geometry, appearance, and physical properties from videos, outperforming existing methods.

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Dan Wang, Xinrui Cui, Serge Belongie, Ravi Ramamoorthi. 2026-02-21. PhysConvex: Physics-Informed 3D Dynamic Convex Radiance Fields for Reconstruction and Simulation. https://arxiv.org/abs/2602.18886

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