A Neural-Network-Based Approach for Loose-Fitting Clothing
Abstract
Since loose-fitting clothing contains dynamic modes that have proven to be difficult to predict via neural networks, we first illustrate how to coarsely approximate these modes with a real-time numerical algorithm specifically designed to mimic the most important ballistic features of a classical numerical simulation. Although there is some flexibility in the choice of the numerical algorithm used as a proxy for full simulation, it is essential that the stability and accuracy be independent from any time step restriction or similar requirements in order to facilitate real-time performance. In order to reduce the number of degrees of freedom that require approximations to their dynamics, we simulate rigid frames and use skinning to reconstruct a rough approximation to a desirable mesh; as one might expect, neural-network-based skinning seems to perform better than linear blend skinning in this scenario. Improved high frequency deformations are subsequently added to the skinned mesh via a quasistatic neural network (QNN). In contrast to recurrent neural networks that require a plethora of training data in order to adequately generalize to new examples, QNNs perform well with significantly less training data.
Cite
@article{arxiv.2404.16896,
title = {A Neural-Network-Based Approach for Loose-Fitting Clothing},
author = {Yongxu Jin and Dalton Omens and Zhenglin Geng and Joseph Teran and Abishek Kumar and Kenji Tashiro and Ronald Fedkiw},
journal= {arXiv preprint arXiv:2404.16896},
year = {2024}
}