  We present a novel up-resing technique for generating high-resolution liquids
based on scene flow estimation using deep neural networks. Our approach infers
and synthesizes small- and large-scale details solely from a low-resolution
particle-based liquid simulation. The proposed network leverages neighborhood
contributions to encode inherent liquid properties throughout convolutions. We
also propose a particle-based approach to interpolate between liquids generated
from varying simulation discretizations using a state-of-the-art bidirectional
optical flow solver method for fluids in addition to a novel key-event
topological alignment constraint. In conjunction with the neighborhood
contributions, our loss formulation allows the inference model throughout
epochs to reward important differences in regard to significant gaps in
simulation discretizations. Even when applied in an untested simulation setup,
our approach is able to generate plausible high-resolution details. Using this
interpolation approach and the predicted displacements, our approach combines
the input liquid properties with the predicted motion to infer semi-Lagrangian
advection. We furthermore showcase how the proposed interpolation approach can
facilitate generating large simulation datasets with a subset of initial
condition parameters.
