Semantics-Aware Color Candidate Generation for Graphic Designs

Abstract

In this paper, we propose a novel color-suggestion method considering the semantics of graphic design to support the starting point in color design. Although color design is important in design production, the standard OS color palette provided by paint applications has too many choices, which often leads to confusion in selection. Therefore, we propose a machine learning-based color generation model that can generate multiple color candidatesusing the layer names set for each layer of a given graphic design. Our method presents candidate colors to the users by considering the semantics of the graphic design for each layer. Therefore, the users can obtain the starting color of the graphic design exploration, and can obtain diverse designs. We implement a web application using our method and evaluate the extent to which the method supports the design process of novice users.