  We propose a method that leverages graph neural networks, multi-level message
passing, and unsupervised training to enable real-time prediction of realistic
clothing dynamics. Whereas existing methods based on linear blend skinning must
be trained for specific garments, our method is agnostic to body shape and
applies to tight-fitting garments as well as loose, free-flowing clothing. Our
method furthermore handles changes in topology (e.g., garments with buttons or
zippers) and material properties at inference time. As one key contribution, we
propose a hierarchical message-passing scheme that efficiently propagates stiff
stretching modes while preserving local detail. We empirically show that our
method outperforms strong baselines quantitatively and that its results are
perceived as more realistic than state-of-the-art methods.
