Video Interpolation via Generalized Deformable Convolution
Video interpolation aims at increasing the frame rate of a given video by synthesizing intermediate frames. The existing video 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 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.
ArXiv