SpringTime: Learning Simulatable Models of Cloth with Spatially-varying Constitutive Properties

TL;DR

SpringTime introduces a data-driven surrogate model for cloth simulation that captures complex, spatially-varying material properties efficiently, avoiding common numerical artifacts and outperforming existing neural methods in speed and accuracy.

Contribution

The paper presents SpringTime, a novel mass-spring network framework that learns cloth material properties directly from motion data, improving simulation speed and accuracy while eliminating membrane locking artifacts.

Findings

Accurately models spatially-varying cloth materials from motion data.

Achieves faster training and higher accuracy than graph-based and neural ODE models.

Demonstrates robustness and generalization in diverse dynamic scenarios.

Abstract

Materials used in real clothing exhibit remarkable complexity and spatial variation due to common processes such as stitching, hemming, dyeing, printing, padding, and bonding. Simulating these materials, for instance using finite element methods, is often computationally demanding and slow. Worse, such methods can suffer from numerical artifacts called ``membrane locking'' that makes cloth appear artificially stiff. Here we propose a general framework, called SpringTime, for learning a simple yet efficient surrogate model that captures the effects of these complex materials using only motion observations. The cloth is discretized into a mass-spring network with unknown material parameters that are learned directly from the motion data, using a novel force-and-impulse loss function. Our approach demonstrates the ability to accurately model spatially varying material properties from a variety of data sources, and immunity to membrane locking which plagues FEM-based simulations. Compared to graph-based networks and neural ODE-based architectures, our method achieves significantly faster training times, higher reconstruction accuracy, and improved generalization to novel dynamic scenarios. Codebase for the paper can be found at https://github.com/ericchen321/springtime.