A Neural-Network-Based Approach for Loose-Fitting Clothing
<|reference_start|>A Neural-Network-Based Approach for Loose-Fitting Clothing: 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.<|reference_end|>
@article{jin2024a,
    title={A Neural-Network-Based Approach for Loose-Fitting Clothing},
    author={Yongxu Jin, Dalton Omens, Zhenglin Geng, Joseph Teran, Abishek Kumar,
  Kenji Tashiro, Ronald Fedkiw},
    journal={arXiv preprint arXiv:2404.16896},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16896},
    primaryClass={cs.GR cs.LG}
}