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.

翻译：现实服装所用材料因缝制、卷边、染色、印花、填充和粘合等常见工艺而展现出显著的复杂性和空间变化性。使用有限元方法等仿真此类材料通常计算需求大且速度缓慢。更严重的是，这类方法可能遭受称为“膜锁定”的数值伪影影响，导致布料呈现非真实的僵硬效果。本文提出名为SpringTime的通用框架，通过学习仅基于运动观测数据的简化高效代理模型来捕捉这些复杂材料的力学效应。该方法将布料离散化为具有未知材料参数的质点弹簧网络，通过新颖的力-冲量损失函数直接从运动数据中学习参数。我们的方法展示了从多种数据源准确建模空间变化材料特性的能力，并避免了困扰基于有限元仿真的膜锁定问题。与基于图神经网络和神经微分方程的架构相比，本方法实现了显著更快的训练速度、更高的重建精度，以及对新颖动态场景更强的泛化能力。论文代码库位于https://github.com/ericchen321/springtime。