We present a neural network-based simulation super-resolution framework that
can efficiently and realistically enhance a facial performance produced by a
low-cost, realtime physics-based simulation to a level of detail that closely
approximates that of a reference-quality off-line simulator with much higher
resolution (26x element count in our examples) and accurate physical modeling.
Our approach is rooted in our ability to construct - via simulation - a
training set of paired frames, from the low- and high-resolution simulators
respectively, that are in semantic correspondence with each other. We use face
animation as an exemplar of such a simulation domain, where creating this
semantic congruence is achieved by simply dialing in the same muscle actuation
controls and skeletal pose in the two simulators. Our proposed neural network
super-resolution framework generalizes from this training set to unseen
expressions, compensates for modeling discrepancies between the two simulations
due to limited resolution or cost-cutting approximations in the real-time
variant, and does not require any semantic descriptors or parameters to be
provided as input, other than the result of the real-time simulation. We
evaluate the efficacy of our pipeline on a variety of expressive performances
and provide comparisons and ablation experiments for plausible variations and
alternatives to our proposed scheme.

本文通过神经网络模拟超分辨率框架，可以实现将低成本、实时物理模拟产生的面部表现有效、逼真地提升到接近高分辨率（我们例子中元素数量高出 26 倍）的参考质量，同时补偿了实时模拟的限制和成本削减近似的建模差异，不需要提供任何语义描述符或参数，除了实时模拟的结果。