FNOPT: Resolution-Agnostic, Self-Supervised Cloth Simulation using Meta-Optimization with Fourier Neural Operators

TL;DR

FNOpt introduces a resolution-agnostic, self-supervised cloth simulation method using Fourier neural operators, enabling stable, accurate, and detailed cloth dynamics across various mesh resolutions without retraining.

Contribution

The paper proposes a novel FNO-based meta-optimization framework for cloth simulation that generalizes across resolutions and motion patterns without requiring extensive ground truth data.

Findings

Outperforms prior methods in accuracy and robustness

Generalizes well to finer mesh resolutions

Captures fine-scale wrinkles effectively

Abstract

We present FNOpt, a self-supervised cloth simulation framework that formulates time integration as an optimization problem and trains a resolution-agnostic neural optimizer parameterized by a Fourier neural operator (FNO). Prior neural simulators often rely on extensive ground truth data or sacrifice fine-scale detail, and generalize poorly across resolutions and motion patterns. In contrast, FNOpt learns to simulate physically plausible cloth dynamics and achieves stable and accurate rollouts across diverse mesh resolutions and motion patterns without retraining. Trained only on a coarse grid with physics-based losses, FNOpt generalizes to finer resolutions, capturing fine-scale wrinkles and preserving rollout stability. Extensive evaluations on a benchmark cloth simulation dataset demonstrate that FNOpt outperforms prior learning-based approaches in out-of-distribution settings in both accuracy and robustness. These results position FNO-based meta-optimization as a compelling alternative to previous neural simulators for cloth, thus reducing the need for curated data and improving cross-resolution reliability.