NeuralGarSim: Geometry-agnostic Garment Simulation with Neural Fields

Most existing mesh-based methods for garment simulationsuffer from sensitivities tied to mesh discretisation and resolution. Re-cent approaches such as NeuralClothSim employ continuous neural fieldsas a promising alternative, but remain constrained by a 2D curvilinearparameterisation that models a single cloth panel, limiting their abil-ity to represent garments with multiple panels stitched together. Thispaper introduces NeuralGarSim, a quasistatic garment simulator for-mulated directly in 3D Euclidean space. Our method accepts diverseinput representations—including distance fields, meshes, point clouds,and Gaussians—as undeformed garment states, making it compatible withoff-the-shelf reconstruction frameworks without additional preprocessing.We then represent the garment deformation as a neural field and define anonlinear Kirchhoff–Love shell model directly in R3 by applying tangentialdifferential calculus on the garment surface. This ensures that the neuralgarment simulation is parameterisation-space-free and naturally extendsto arbitrary topologies. By minimising a potential energy functional, ourmethod learns a 3D neural deformation field that predicts physicallyconsistent deformations across garments with multiple panels, seams,and holes. It supports multiple material models and trains 2–4× fasterthan its predecessor, NeuralClothSim, while retaining the continuous,consistent, and memory-adaptive behaviour1 .