用生成图像找设计配色，让颜色更贴合概念语义。
GenColor: Generative Color-Concept Association in Visual Design
- 通过文生图模型生成概念图像，再提取相关区域颜色
- 能准确关联主色与点缀色，如'清澈'与'浑浊'的天空配色
- 适合需要语义化配色的设计场景，如品牌或界面设计
现有颜色-概念关联方法依赖查询图像参考和从中提取颜色，但仅对常见概念有效，且受图像质量与参考不稳影响。我们对设计师的调研表明，设计需主色与点缀色搭配，且颜色应随语境变化（如‘清澈’与‘污染’的天空）。为此，我们提出一种生成式方法：利用文本生成图像模型生成概念样本，通过文本引导图像分割定位相关区域，再提取主要与辅助颜色。定量对比专家设计验证了方法有效性，并在多个设计场景和画廊案例中展示其应用潜力。
原文摘要 · Abstract (English)
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.
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