  A majority of methods for video frame interpolation compute bidirectional
optical flow between adjacent frames of a video, followed by a suitable warping
algorithm to generate the output frames. However, approaches relying on optical
flow often fail to model occlusions and complex non-linear motions directly
from the video and introduce additional bottlenecks unsuitable for widespread
deployment. We address these limitations with FLAVR, a flexible and efficient
architecture that uses 3D space-time convolutions to enable end-to-end learning
and inference for video frame interpolation. Our method efficiently learns to
reason about non-linear motions, complex occlusions and temporal abstractions,
resulting in improved performance on video interpolation, while requiring no
additional inputs in the form of optical flow or depth maps. Due to its
simplicity, FLAVR can deliver 3x faster inference speed compared to the current
most accurate method on multi-frame interpolation without losing interpolation
accuracy. In addition, we evaluate FLAVR on a wide range of challenging
settings and consistently demonstrate superior qualitative and quantitative
results compared with prior methods on various popular benchmarks including
Vimeo-90K, UCF101, DAVIS, Adobe, and GoPro. Finally, we demonstrate that FLAVR
for video frame interpolation can serve as a useful self-supervised pretext
task for action recognition, optical flow estimation, and motion magnification.
