Multi-scale Flow-based Occluding Effect and Content Separation for Cartoon Animations.

Occluding effects have been frequently used to present weather conditions and environments in cartoon animations, such as raining, snowing, moving leaves, and moving petals. While these effects greatly enrich the visual appeal of the cartoon animations, they may also cause undesired occlusions on the content area, which significantly complicate the analysis and processing of the cartoon animations. In this paper, we make the first attempt to separate the occluding effects and content for cartoon animations. The major challenge of this problem is that, unlike natural effects that are realistic and small-sized, the effects of cartoons are usually stylistic and large-sized. Besides, effects in cartoons are manually drawn, so their motions are more unpredictable than realistic effects. To separate occluding effects and content for cartoon animations, we propose to leverage the difference in the motion patterns of the effects and the content, and capture the locations of the effects based on a multi-scale flow-based effect prediction (MFEP) module. A dual-task learning system is designed to extract the effect video and reconstruct the effect-removed content video at the same time. We apply our method on a large number of cartoon videos of different content and effects. Experiments show that our method significantly outperforms the existing methods. We further demonstrate how the separated effects and content facilitate the analysis and processing of cartoon videos through different applications, including segmentation, inpainting, and effect migration.