FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes
Summary
This paper presents FreeForm, a reduced-order simulation method for deformable hyperelastic objects using a Reproducing Kernel Particle Method (RKPM) that achieves 40x faster training and lower error than neural field approaches.
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Paper page - FreeForm: Reduced-Order Deformable Simulation from Particle-Based Skinning Eigenmodes
Source: https://huggingface.co/papers/2605.29318
Abstract
Reduced-order simulation of deformable hyperelastic objects using Reproducing Kernel Particle Method achieves faster training and lower error compared to neural field approaches while supporting various geometric representations.
We present a novel formulation formesh-free,reduced-order simulationof deformable hyperelastic objects. Existing work in reduced-order elastodynamic simulation represents the input geometry by either meshes, which can be difficult to obtain due to challenges in scanning and triangulating complex shapes, or byneural fieldsthat require per-shape optimization. We propose to adopt aReproducing Kernel Particle Method(RKPM) representation, which enables the construction of reduced-order skinning weights by solving ageneralized eigensystemon theHessian matrixof theelastic energy. We demonstrate that this formulation not only leads to a 40x training speedup compared with the per-shape optimization ofneural fields, but also achieves lower simulation error when evaluated against the converged results offinite element method. We show our simulation results on a wide variety of objects in different representations including meshes and Gaussian splats, as well as the application of our method in the downstream task ofrobot simulation.
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