Efficient Cloth Simulation using Miniature Cloth and Upscaling Deep Neural Networks
Graphics
2019-07-10 v1 Machine Learning
Abstract
Cloth simulation requires a fast and stable method for interactively and realistically visualizing fabric materials using computer graphics. We propose an efficient cloth simulation method using miniature cloth simulation and upscaling Deep Neural Networks (DNN). The upscaling DNNs generate the target cloth simulation from the results of physically-based simulations of a miniature cloth that has similar physical properties to those of the target cloth. We have verified the utility of the proposed method through experiments, and the results demonstrate that it is possible to generate fast and stable cloth simulations under various conditions.
Cite
@article{arxiv.1907.03953,
title = {Efficient Cloth Simulation using Miniature Cloth and Upscaling Deep Neural Networks},
author = {Tae Min Lee and Young Jin Oh and In-Kwon Lee},
journal= {arXiv preprint arXiv:1907.03953},
year = {2019}
}
Comments
11 pages, 15 figures, 8 tables