Hollowed-Out Icon Colorization with Pre-trained Large-Scale Image Generation Model

Icons are utilized to visually elucidate functions and objects, and are frequently employed on websites. Forweb developers with limited design skills, producing a substantial volume of icons poses a challenge. Consequently,there is a demand for automated methods to generate icons. The process of creating icons involves two stages: linedrawing and coloring. Recent research on automating the coloring process through deep learning has been active.However, existing methods fail to recognize the entities represented by line drawings, leading to inaccuracies inreflecting the target’s structure during the coloring process. A notable limitation of these methods is their inability toaccurately color line drawings of icons with hollow structures, such as donuts. In this study, we introduce a method forcoloring hollowed-out icons, ensuring adherence to the original line drawings. Specifically, we employ a large-scalepre-trained model to generate icons from line drawings, supplemented by reference images and textual descriptions.To facilitate experimental implementation, we compiled a dataset comprising gradient style icons and their associatedtexts. The results of our experimental evaluation indicate that our method surpasses existing approaches in morethan one metrics. Furthermore, the outcomes of the coloring experiments reveal that our method faces challenges incoloring efficiency and highlight the limitations of incorporating text as an auxiliary input for the coloring process.