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。与现有从非严格设置下捕获数据中学习的方法不同，我们主张从严格测试的测量协议获取的数据中学习，并寻找布料的合理物理参数。然而此类数据目前尚不存在，因此我们首先构建了一个具有精确布料测量的新数据集。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使用的数据量。为从小样本数据中学习，我们提出了一个新的贝叶斯可微布料模型来估计真实布料的复杂材料异质性。该模型能从极有限的数据样本中实现高精度数字化。通过详尽的评估与比较，我们证明本方法在布料数字化中具有准确性，在小样本数据学习方面具有高效性，在捕捉材料变化方面具有泛化能力。代码与数据集已在 https://github.com/realcrane/Bayesian-Differentiable-Physics-for-Cloth-Digitalization 开源。