HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics
This addresses the need for generalized clothing simulation in graphics and animation, offering a method that handles diverse garments without retraining.
The paper tackles the problem of real-time prediction of realistic clothing dynamics across various garments and body shapes, achieving results perceived as more realistic than state-of-the-art methods.
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.