Dynamic Topology
Its' graph-based nature allows HOOD to model garments with changing topology by toggling specific edges in the garment mesh.
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
We extend the graph-based message-passing architecture of MeshGraphNets with hierarchical component. Several levels of long-range edges allow the signal from each garment node to propagate father enabling the model to better model long-range dependencies in large garments.
Its' graph-based nature allows HOOD to model garments with changing topology by toggling specific edges in the garment mesh.
By controlling local material parameters we can model garments make of different fabrics.
@inproceedings{grigorev2022hood,
author = {Grigorev, Artur and Thomaszewski, Bernhard and Black, Michael J and Hilliges, Otmar},
title = {{HOOD}: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics},
journal = {Computer Vision and Pattern Recognition (CVPR)},
year = {2023},
}