MGN: multilayered garment animation generation neural network

This paper presents a multilayered garment animation generation method. Generating realistic dynamics in 3D garment animations is a challenging task due to the complex nature of multilayered garments and the variety of outer forces involved. Existing data-driven approaches have mainly focused on the study of static draping deformation of multilayer garments, with less consideration for the temporal deformation of garments, such as the time-varying motion behaviors of individual layers and their continuous interactions during motion. In addition, these methods require a substantial amount of high-quality paired garment datasets for network training, leading to a costly data acquisition and annotation process. To address these challenges, we propose a multilayered garment animation generation method that explicitly models different garment layers as separate meshes, and employs a combination of unsupervised and temporally supervised learning strategies to analyze and model the behavior of individual garment layers and their interactions. Our primary contribution lies in introducing a two-stage network architecture for layered garment processing, which decomposes multilayer garment deformation prediction into single-layer garment generation and interlayer garment interaction deformation. We focus more on generating two-layered clothing animations. Of course, our two-layered approach can be used iteratively to support more layers by using the current outer layer as the inner layer for the next iteration. This approach achieves dynamic simulation of multilayer garments, and experimental results demonstrate that our method can generate realistic multilayer garment deformation effects, outperforming existing methods both visually and in terms of evaluation metrics.