We propose a new method for cloth digitalization. Deviating from existing methods which learn from data captured under relatively casual settings, we propose to learn from data captured in strictly tested measuring protocols, and find plausible physical parameters of the cloths. However, such data is currently absent, so we first propose a new dataset with accurate cloth measurements. Further, the data size is considerably smaller than the ones in current deep learning, due to the nature of the data capture process. To learn from small data, we propose a new Bayesian differentiable cloth model to estimate the complex material heterogeneity of real cloths. It can provide highly accurate digitalization from very limited data samples. Through exhaustive evaluation and comparison, we show our method is accurate in cloth digitalization, efficient in learning from limited data samples, and general in capturing material variations. Code and data are available https://github.com/realcrane/Bayesian-Differentiable-Physics-for-Cloth-Digitalization

翻译：我们提出了一种新的布料数字化方法。与现有方法从相对随意设置下采集的数据中学习不同，我们提出从严格测试的测量协议采集的数据中学习，并寻找布料的合理物理参数。然而，目前此类数据尚缺失，因此我们首先提出一个包含精确布料测量的新数据集。此外，由于数据采集过程的特性，该数据集规模远小于当前深度学习所用的数据集。为从小样本数据中学习，我们提出一种新的贝叶斯可微分布料模型，用于估计真实布料的复杂材料异质性。该模型能从极少的数据样本中提供高度准确的数字化结果。通过详尽的评估与比较，我们证明该方法在布料数字化方面精确、能从有限数据样本中高效学习，并具有捕捉材料变化的一般性能力。代码与数据见 https://github.com/realcrane/Bayesian-Differentiable-Physics-for-Cloth-Digitalization