  Most approaches for video frame interpolation require accurate dense
correspondences to synthesize an in-between frame. Therefore, they do not
perform well in challenging scenarios with e.g. lighting changes or motion
blur. Recent deep learning approaches that rely on kernels to represent motion
can only alleviate these problems to some extent. In those cases, methods that
use a per-pixel phase-based motion representation have been shown to work well.
However, they are only applicable for a limited amount of motion. We propose a
new approach, PhaseNet, that is designed to robustly handle challenging
scenarios while also coping with larger motion. Our approach consists of a
neural network decoder that directly estimates the phase decomposition of the
intermediate frame. We show that this is superior to the hand-crafted
heuristics previously used in phase-based methods and also compares favorably
to recent deep learning based approaches for video frame interpolation on
challenging datasets.
