  Prevailing video frame interpolation algorithms, that generate the
intermediate frames from consecutive inputs, typically rely on complex model
architectures with heavy parameters or large delay, hindering them from diverse
real-time applications. In this work, we devise an efficient encoder-decoder
based network, termed IFRNet, for fast intermediate frame synthesizing. It
first extracts pyramid features from given inputs, and then refines the
bilateral intermediate flow fields together with a powerful intermediate
feature until generating the desired output. The gradually refined intermediate
feature can not only facilitate intermediate flow estimation, but also
compensate for contextual details, making IFRNet do not need additional
synthesis or refinement module. To fully release its potential, we further
propose a novel task-oriented optical flow distillation loss to focus on
learning the useful teacher knowledge towards frame synthesizing. Meanwhile, a
new geometry consistency regularization term is imposed on the gradually
refined intermediate features to keep better structure layout. Experiments on
various benchmarks demonstrate the excellent performance and fast inference
speed of proposed approaches. Code is available at
https://github.com/ltkong218/IFRNet.
