Video frame interpolation aims at synthesizing intermediate frames from
nearby source frames while maintaining spatial and temporal consistencies. The
existing deep-learning-based video frame interpolation methods can be roughly
divided into two categories: flow-based methods and kernel-based methods. The
performance of flow-based methods is often jeopardized by the inaccuracy of
flow map estimation due to oversimplified motion models, while that of
kernel-based methods tends to be constrained by the rigidity of kernel shape.
To address these performance-limiting issues, a novel mechanism named
generalized deformable convolution is proposed, which can effectively learn
motion information in a data-driven manner and freely select sampling points in
space-time. We further develop a new video frame interpolation method based on
this mechanism. Our extensive experiments demonstrate that the new method
performs favorably against the state-of-the-art, especially when dealing with
complex motions.

本论文提出了一种基于广义可变形卷积的新的视频帧插值方法，该方法可以学习运动信息并自由选择时空采样点，经过广泛的实验证明，该方法在处理复杂运动时表现出类似于最先进技术的性能。