A Neural-Network-Based Approach for Loose-Fitting Clothing
This work addresses real-time simulation of clothing dynamics for applications like animation or virtual reality, representing an incremental improvement in efficiency and data requirements.
The paper tackles the challenge of predicting dynamic modes in loose-fitting clothing by combining a real-time numerical algorithm with neural-network-based skinning and a quasistatic neural network, achieving improved performance with less training data compared to recurrent neural networks.
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.