Multimodal Color Recommendation in Vector Graphic Documents
Qianru Qiu Xueting Wang Mayu Otani
Color selection plays a critical role in graphic document design and requires
sufficient consideration of various contexts. However, recommending appropriate
colors which harmonize with the other colors and textual contexts in documents
is a challenging task, even for experienced designers. In this study, we
propose a multimodal masked color model that integrates both color and textual
contexts to provide text-aware color recommendation for graphic documents. Our
proposed model comprises self-attention networks to capture the relationships
between colors in multiple palettes, and cross-attention networks that
incorporate both color and CLIP-based text representations. Our proposed method
primarily focuses on color palette completion, which recommends colors based on
the given colors and text. Additionally, it is applicable for another color
recommendation task, full palette generation, which generates a complete color
palette corresponding to the given text. Experimental results demonstrate that
our proposed approach surpasses previous color palette completion methods on
accuracy, color distribution, and user experience, as well as full palette
generation methods concerning color diversity and similarity to the ground
truth palettes.