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Existing deep interactive colorization models have focused on ways to utilizevarious types of interactions, such as point-wise color hints, scribbles, ornatural-language texts, as methods to reflect a user's intent at runtime.However, another approach, which actively informs the user of the mosteffective regions to give hints for sketch image colorization, has beenunder-explored. This paper proposes a novel model-guided deep interactivecolorization framework that reduces the required amount of user interactions,by prioritizing the regions in a colorization model. Our method, calledGuidingPainter, prioritizes these regions where the model most needs a colorhint, rather than just relying on the user's manual decision on where to give acolor hint. In our extensive experiments, we show that our approach outperformsexisting interactive colorization methods in terms of the conventional metrics,such as PSNR and FID, and reduces required amount of interactions.

Guiding Users to Where to Give Color Hints for Efficient Interactive Sketch Colorization via Unsupervised Region Prioritization