GenColor: Generative Color-Concept Association in Visual Design
This addresses the need for context-dependent and primary-accent color compositions in design, offering a solution for designers, but it is incremental as it builds on existing text-to-image models.
The paper tackles the problem of generating color-concept associations for visual design by introducing a generative approach that uses text-to-image models to mine semantically resonant colors, validated through quantitative comparisons with expert designs.
Existing approaches for color-concept association typically rely on query-based image referencing, and color extraction from image references. However, these approaches are effective only for common concepts, and are vulnerable to unstable image referencing and varying image conditions. Our formative study with designers underscores the need for primary-accent color compositions and context-dependent colors (e.g., 'clear' vs. 'polluted' sky) in design. In response, we introduce a generative approach for mining semantically resonant colors leveraging images generated by text-to-image models. Our insight is that contemporary text-to-image models can resemble visual patterns from large-scale real-world data. The framework comprises three stages: concept instancing produces generative samples using diffusion models, text-guided image segmentation identifies concept-relevant regions within the image, and color association extracts primarily accompanied by accent colors. Quantitative comparisons with expert designs validate our approach's effectiveness, and we demonstrate the applicability through cases in various design scenarios and a gallery.