We propose a novel approach for deformation-aware neural networks that learn
the weighting and synthesis of dense volumetric deformation fields. Our method
specifically targets the space-time representation of physical surfaces from
liquid simulations. Liquids exhibit highly complex, non-linear behavior under
changing simulation conditions such as different initial conditions. Our
algorithm captures these complex phenomena in two stages: a first neural
network computes a weighting function for a set of pre-computed deformations,
while a second network directly generates a deformation field for refining the
surface. Key for successful training runs in this setting is a suitable loss
function that encodes the effect of the deformations, and a robust calculation
of the corresponding gradients. To demonstrate the effectiveness of our
approach, we showcase our method with several complex examples of flowing
liquids with topology changes. Our representation makes it possible to rapidly
generate the desired implicit surfaces. We have implemented a mobile
application to demonstrate that real-time interactions with complex liquid
effects are possible with our approach.

提出了一种新颖的变形感知神经网络方法，用于学习密集体积变形场的加权合成，特别针对液体模拟的时空表征，成功地实现了实时流体效果的移动端交互。