  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.
