A Neural-Network-Based Approach for Loose-Fitting Clothing

• URL: http://arxiv.org/abs/2404.16896v1
• Date: Thu, 25 Apr 2024 05:52:20 GMT
• Title: A Neural-Network-Based Approach for Loose-Fitting Clothing
• Authors: Yongxu Jin, Dalton Omens, Zhenglin Geng, Joseph Teran, Abishek Kumar, Kenji Tashiro, Ronald Fedkiw,
• Abstract summary: We show how to approximate dynamic modes in loose-fitting clothing using a real-time numerical algorithm. We also use skinning to reconstruct a rough approximation to a desirable mesh. In contrast to recurrent neural networks that require a plethora of training data, QNNs perform well with significantly less training data.
• Score: 2.910739621411222
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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