Revisiting Adaptive Convolutions for Video Frame Interpolation
This work addresses the challenge of determining key factors for interpolation quality in video processing, but it is incremental as it builds on an older method.
The paper tackled video frame interpolation by revisiting adaptive separable convolutions with low-level improvements, achieving near state-of-the-art results, though specific numbers are not provided.
Video frame interpolation, the synthesis of novel views in time, is an increasingly popular research direction with many new papers further advancing the state of the art. But as each new method comes with a host of variables that affect the interpolation quality, it can be hard to tell what is actually important for this task. In this work, we show, somewhat surprisingly, that it is possible to achieve near state-of-the-art results with an older, simpler approach, namely adaptive separable convolutions, by a subtle set of low level improvements. In doing so, we propose a number of intuitive but effective techniques to improve the frame interpolation quality, which also have the potential to other related applications of adaptive convolutions such as burst image denoising, joint image filtering, or video prediction.