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Video Frame Interpolation (VFI) is a fascinating and challenging problem in the computer vision (CV) field, aiming to generate non-existing frames between two consecutive video frames. In recent years, many algorithms based on optical flow, kernel, or phase information have been proposed. In this article, we provide a comprehensive review of recent developments in the VFI technique. We first introduce the history of VFI algorithms’ development, the evaluation metrics, and publicly available datasets. We then compare each algorithm in detail, point out their advantages and disadvantages, and compare their interpolation performance and speed on different remarkable datasets. VFI technology has drawn continuous attention in the CV community, some video processing applications based on VFI are also mentioned in this survey, such as slow-motion generation, video compression, video restoration. Finally, we outline the bottleneck faced by the current video frame interpolation technology and discuss future research work.
Dong et al. (Tue,) studied this question.