Beyond Natural Motion: Exploring Discontinuity for Video Frame Interpolation

Video interpolation is the task that synthesizes the intermediate frame given two consecutive frames. Most of the previous studies have focused on appropriate frame warping operations and refinement modules for the warped frames. These studies have been conducted on natural videos having only continuous motions. However, many practical videos contain a lot of discontinuous motions, such as chat windows, watermarks, GUI elements, or subtitles. We propose three techniques to expand the concept of transition between two consecutive frames to address these issues. First is a new architecture that can separate continuous and discontinuous motion areas. We also propose a novel data augmentation strategy called figure-text mixing (FTM) to make our model learn more general scenarios. Finally, we propose loss functions to give supervisions of the discontinuous motion areas with the data augmentation. We collected a special dataset consisting of some mobile games and chatting videos. We show that our method significantly improves the interpolation qualities of the videos on the special dataset. Moreover, our model outperforms the state-of-the-art methods for natural video datasets containing only continuous motions, such as DAVIS and UCF101.