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Adjusting the photo color to associate with some design elements is anessential way for a graphic design to effectively deliver its message and makeit aesthetically pleasing. However, existing tools and previous works face adilemma between the ease of use and level of expressiveness. To this end, weintroduce an interactive language-based approach for photo recoloring, whichprovides an intuitive system that can assist both experts and novices ongraphic design. Given a graphic design containing a photo that needs to berecolored, our model can predict the source colors and the target regions, andthen recolor the target regions with the source colors based on the givenlanguage-based instruction. The multi-granularity of the instruction allowsdiverse user intentions. The proposed novel task faces several uniquechallenges, including: 1) color accuracy for recoloring with exactly the samecolor from the target design element as specified by the user; 2)multi-granularity instructions for parsing instructions correctly to generate aspecific result or multiple plausible ones; and 3) locality for recoloring insemantically meaningful local regions to preserve original image semantics. Toaddress these challenges, we propose a model called LangRecol with two maincomponents: the language-based source color prediction module and thesemantic-palette-based photo recoloring module. We also introduce an approachfor generating a synthetic graphic design dataset with instructions to enablemodel training. We evaluate our model via extensive experiments and userstudies. We also discuss several practical applications, showing theeffectiveness and practicality of our approach. Code and data for this paperare at: https://zhenwwang.github.io/langrecol.

Language-based Photo Color Adjustment for Graphic Designs