Learning Simulatable Models of Cloth with Spatially-varying Constitutive Properties
This addresses the computational demands and artifacts in cloth simulation for applications like animation or virtual reality, representing a novel method for a known bottleneck.
The paper tackled the problem of simulating complex, spatially-varying cloth materials efficiently by proposing a Mass-Spring Net framework that learns surrogate models from motion observations, achieving faster training times, higher accuracy, and immunity to membrane locking compared to existing methods.
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 Mass-Spring Net, 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.