Cao, RuizhiMo, HaoranGao, ChengyingZhang, Fang-Lue and Eisemann, Elmar and Singh, Karan2021-10-142021-10-1420211467-8659https://doi.org/10.1111/cgf.14396https://diglib.eg.org:443/handle/10.1111/cgf14396Automatic line art colorization plays an important role in anime and comic industry. While existing methods for line art colorization are able to generate plausible colorized results, they tend to suffer from the color bleeding issue. We introduce an explicit segmentation fusion mechanism to aid colorization frameworks in avoiding color bleeding artifacts. This mechanism is able to provide region segmentation information for the colorization process explicitly so that the colorization model can learn to avoid assigning the same color across regions with different semantics or inconsistent colors inside an individual region. The proposed mechanism is designed in a plug-and-play manner, so it can be applied to a diversity of line art colorization frameworks with various kinds of user guidances. We evaluate this mechanism in tag-based and referencebased line art colorization tasks by incorporating it into the state-of-the-art models. Comparisons with these existing models corroborate the effectiveness of our method which largely alleviates the color bleeding artifacts. The code is available at https://github.com/Ricardo-L-C/ColorizationWithRegion.Computing methodologiesImage manipulationNeural networksComputer vision10.1111/cgf.143961-10