Hierarchical Graphs for Generalized Modelling of Clothing Dynamics

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Hierarchical Graphs for Generalized Modelling of Clothing Dynamics

Teasera hood — We combine graph neural networks, a hierarchical graph representation, and multi-level message passing with an unsupervised training scheme to enable efficient prediction of realistic clothing dynamics for arbitrary types of garments and body shapes. Our method models both tight-fitting and free-flowing clothes draped over arbitrary body shapes. At test time, the method generalizes to new, entirely unseen, garments (left), and allows dynamic and unconstrained poses (right) and changes in material parameters and topology.

We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable efficient prediction of realistic clothing dynamics. Whereas existing methods based on linear blend skinning must be trained for specific garments, our method, called HOOD, is agnostic to body shape and applies to tight-fitting garments as well as loose, free-flowing clothing. Furthermore, HOOD 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 HOOD outperforms strong baselines quantitatively and that its results are perceived as more realistic than state-of-the-art methods.