  Video frame interpolation, the synthesis of novel views in time, is an
increasingly popular research direction with many new papers further advancing
the state of the art. But as each new method comes with a host of variables
that affect the interpolation quality, it can be hard to tell what is actually
important for this task. In this work, we show, somewhat surprisingly, that it
is possible to achieve near state-of-the-art results with an older, simpler
approach, namely adaptive separable convolutions, by a subtle set of low level
improvements. In doing so, we propose a number of intuitive but effective
techniques to improve the frame interpolation quality, which also have the
potential to other related applications of adaptive convolutions such as burst
image denoising, joint image filtering, or video prediction.
