  DNN-based frame interpolation--that generates the intermediate frames given
two consecutive frames--typically relies on heavy model architectures with a
huge number of features, preventing them from being deployed on systems with
limited resources, e.g., mobile devices. We propose a compression-driven
network design for frame interpolation (CDFI), that leverages model pruning
through sparsity-inducing optimization to significantly reduce the model size
while achieving superior performance. Concretely, we first compress the
recently proposed AdaCoF model and show that a 10X compressed AdaCoF performs
similarly as its original counterpart; then we further improve this compressed
model by introducing a multi-resolution warping module, which boosts visual
consistencies with multi-level details. As a consequence, we achieve a
significant performance gain with only a quarter in size compared with the
original AdaCoF. Moreover, our model performs favorably against other
state-of-the-arts in a broad range of datasets. Finally, the proposed
compression-driven framework is generic and can be easily transferred to other
DNN-based frame interpolation algorithm. Our source code is available at
https://github.com/tding1/CDFI.
