用运动数据学习布料的空间变异性，实现高效逼真模拟。
SpringTime: Learning Simulatable Models of Cloth with Spatially-varying Constitutive Properties
- 通过质量-弹簧网络和力与冲量损失函数，从运动数据中学习材料参数。
- 在多种数据源上准确建模空间变化的布料属性，且无膜锁定问题。
- 相比图神经网络和神经微分方程，训练更快、精度更高、泛化更强。
真实衣物材料因缝制、收边、染色、印花、加厚和粘合等工艺呈现显著复杂性和空间异质性。传统有限元方法虽可模拟，但计算成本高，且易出现导致布料过刚的数值伪影‘膜锁定’。本文提出通用框架SpringTime，仅通过运动观测数据，学习一种简单高效的替代模型，以捕捉复杂材料特性。布料被离散为带有未知材料参数的质量-弹簧网络，参数通过新颖的力与冲量损失函数从运动数据中直接学习。该方法能准确建模多种数据源下的空间变异性，且完全免疫膜锁定。相较于图神经网络和神经微分方程架构，本方法训练速度更快、重建精度更高，并在新动态场景中表现出更优泛化能力。代码已开源：https://github.com/ericchen321/springtime。
原文摘要 · Abstract (English)
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.
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