A Neural-Network-Based Approach for Loose-Fitting Clothing
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911854021836800 |
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| author | Jin, Yongxu Omens, Dalton Geng, Zhenglin Teran, Joseph Kumar, Abishek Tashiro, Kenji Fedkiw, Ronald |
| author_facet | Jin, Yongxu Omens, Dalton Geng, Zhenglin Teran, Joseph Kumar, Abishek Tashiro, Kenji Fedkiw, Ronald |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16896 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Neural-Network-Based Approach for Loose-Fitting Clothing Jin, Yongxu Omens, Dalton Geng, Zhenglin Teran, Joseph Kumar, Abishek Tashiro, Kenji Fedkiw, Ronald Graphics Machine Learning 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. |
| title | A Neural-Network-Based Approach for Loose-Fitting Clothing |
| topic | Graphics Machine Learning |
| url | https://arxiv.org/abs/2404.16896 |