Language-based Photo Color Adjustment for Graphic Designs

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 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. Code and data for this paper are at: https://zhenwwang.github.io/langrecol.