  Frame interpolation is an essential video processing technique that adjusts
the temporal resolution of an image sequence. While deep learning has brought
great improvements to the area of video frame interpolation, techniques that
make use of neural networks can typically not easily be deployed in practical
applications like a video editor since they are either computationally too
demanding or fail at high resolutions. In contrast, we propose a deep learning
approach that solely relies on splatting to synthesize interpolated frames.
This splatting-based synthesis for video frame interpolation is not only much
faster than similar approaches, especially for multi-frame interpolation, but
can also yield new state-of-the-art results at high resolutions.
