  Mimicking realistic dynamics in 3D garment animations is a challenging task
due to the complex nature of multi-layered garments and the variety of outer
forces involved. Existing approaches mostly focus on single-layered garments
driven by only human bodies and struggle to handle general scenarios. In this
paper, we propose a novel data-driven method, called LayersNet, to model
garment-level animations as particle-wise interactions in a micro physics
system. We improve simulation efficiency by representing garments as
patch-level particles in a two-level structural hierarchy. Moreover, we
introduce a novel Rotation Equivalent Transformation that leverages the
rotation invariance and additivity of physics systems to better model outer
forces. To verify the effectiveness of our approach and bridge the gap between
experimental environments and real-world scenarios, we introduce a new
challenging dataset, D-LAYERS, containing 700K frames of dynamics of 4,900
different combinations of multi-layered garments driven by both human bodies
and randomly sampled wind. Our experiments show that LayersNet achieves
superior performance both quantitatively and qualitatively. We will make the
dataset and code publicly available at
https://mmlab-ntu.github.io/project/layersnet/index.html .
