  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
