Language-based Photo Color Adjustment for Graphic Designs

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number101
Journal / PublicationACM Transactions on Graphics
Volume42
Issue number4
Online published26 Jul 2023
Publication statusPublished - Aug 2023

Conference

TitleACM SIGGRAPH 2023
Period6 - 10 August 2023

Abstract

Adjusting the photo color to associate with some design elements is an essential way for a graphic design to effectively deliver its message and make it aesthetically pleasing. However, existing tools and previous works face a dilemma between the ease of use and level of expressiveness. To this end, we introduce an interactive language-based approach for photo recoloring, which provides an intuitive system that can assist both experts and novices on graphic design. Given a graphic design containing a photo that needs to be recolored, our model can predict the source colors and the target regions, and then recolor the target regions with the source colors based on the given language-based instruction. The multi-granularity of the instruction allows diverse user intentions. The proposed novel task faces several unique challenges, including: 1) color accuracy for recoloring with exactly the same color from the target design element as specified by the user; 2) multi-granularity instructions for parsing instructions correctly to generate a specific result or multiple plausible ones; and 3) locality for recoloring in semantically meaningful local regions to preserve original image semantics. To address these challenges, we propose a model called LangRecol with two main components: the language-based source color prediction module and the semantic-palette-based photo recoloring module. We also introduce an approach for generating a synthetic graphic design dataset with instructions to enable model training. We evaluate our model via extensive experiments and user studies. We also discuss several practical applications, showing the effectiveness and practicality of our approach. Please find the code and data at https://zhenwwang.github.io/langrecol. © 2023 ACM.

Research Area(s)

  • data-driven graphic design, photo recoloring, language-guided

Citation Format(s)

Language-based Photo Color Adjustment for Graphic Designs. / WANG, Zhenwei; ZHAO, Nanxuan; HANCKE, Gerhard et al.
In: ACM Transactions on Graphics, Vol. 42, No. 4, 101, 08.2023.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review