Language-based Photo Color Adjustment for Graphic Designs
Zhenwei Wang, Nanxuan Zhao, Gerhard Hancke, Rynson W.H. Lau

TL;DR
This paper presents LangRecol, an interactive language-based photo recoloring system that enables intuitive and precise color adjustments in graphic designs, addressing challenges of accuracy, multi-granularity, and locality.
Contribution
The paper introduces a novel model with modules for predicting source colors and recoloring based on language instructions, along with a synthetic dataset for training.
Findings
Effective color accuracy in recoloring tasks
Ability to interpret multi-granularity instructions
Successful application in practical graphic design scenarios
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…
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