  Frame interpolation attempts to synthesise frames given one or more
consecutive video frames. In recent years, deep learning approaches, and
notably convolutional neural networks, have succeeded at tackling low- and
high-level computer vision problems including frame interpolation. These
techniques often tackle two problems, namely algorithm efficiency and
reconstruction quality. In this paper, we present a multi-scale generative
adversarial network for frame interpolation (\mbox{FIGAN}). To maximise the
efficiency of our network, we propose a novel multi-scale residual estimation
module where the predicted flow and synthesised frame are constructed in a
coarse-to-fine fashion. To improve the quality of synthesised intermediate
video frames, our network is jointly supervised at different levels with a
perceptual loss function that consists of an adversarial and two content
losses. We evaluate the proposed approach using a collection of 60fps videos
from YouTube-8m. Our results improve the state-of-the-art accuracy and provide
subjective visual quality comparable to the best performing interpolation
method at x47 faster runtime.
