  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.
