Improved inclusion matching for animation paint bucket colorization

Abstract

The celluloid style is usually characterized by clear lines, distinct color blocks, and sharp contrast between light and dark, etc. When it comes to celluloid-style cartoons, it involves colorizing the line-enclosed segments of line art frame by frame. In the past decades, with the popularization of computer technology, practitioners commonly utilize paint bucket tools to perform line art colorization tasks, based on RGB values predetermined by a color designer. Nevertheless, it is still laborious regarding diverse color segments, segment matching and the large number of frames. Concerning that, a number of automated methodologies have been devised. The methodology named inclusion matching proposed by a group in NTU is advanced and practical. To a large extent, it can effectively address issues like occlusion or wrinkles that arise among frames. The inclusion matching pipeline is based on deep neural networks. From coarse to fine, it starts to warp the line art for extracting features and then performs inclusion matching using the attention mechanism. However, this pipeline ignores the global information of line art. Inspired by the vision transformer, the present study introduces a new mechanism to enhance the inclusion matching module. Experiments depict the effectiveness of our techniques.