We present a novel learning method to predict the cloth deformation for skeleton-based characters with a two-stream network. The characters processed in our approach are not limited to humans, and can be other skeletal-based representations of non-human targets such as fish or pets. We use a novel network architecture which consists of skeleton-based and mesh-based residual networks to learn the coarse and wrinkle features as the overall residual from the template cloth mesh. Our network is used to predict the deformation for loose or tight-fitting clothing or dresses. We ensure that the memory footprint of our network is low, and thereby result in reduced storage and computational requirements. In practice, our prediction for a single cloth mesh for the skeleton-based character takes about 7 milliseconds on an NVIDIA GeForce RTX 3090 GPU. Compared with prior methods, our network can generate fine deformation results with details and wrinkles.
@article{arxiv.2305.18808,
title = {CTSN: Predicting Cloth Deformation for Skeleton-based Characters with a Two-stream Skinning Network},
author = {Yudi Li and Min Tang and Yun Yang and Ruofeng Tong and Shuangcai Yang and Yao Li and Bailin An and Qilong Kou},
journal= {arXiv preprint arXiv:2305.18808},
year = {2023}
}