FlexIcon: Flexible Icon Colorization via Guided Images and Palettes

Shukai Wu, Yuhang Yang, Shuchang Xu, Weiming Liu, Xiao Yan, Sanyuan Zhang

Abstract: Automatic icon colorization systems show great potential value as they can serve as a source of inspiration for designers. Despite yielding promising results, previous reference-guided approaches ignore how to effectively fuse icon structure and style, leading to unpleasant color effects. Meanwhile, they cannot take free-style palettes as inputs, which is less user-friendly. To this end, we present FlexIcon, a Flexible Icon colorization model based on guided images and palettes. To promote visual quality, our model first leverages a Hybrid Multi-expert Module to aggregate better structural features, followed by dynamically integrating the global style with each individual pixel of the structure map via the Pixel-Style Aggregation Layer. We also introduce an efficient learning scheme for free-style palette-based colorization, editing, interpolation, and diverse generation. Extensive experiments demonstrate the superiority of our framework compared with state-of-the-art approaches. In addition, we contribute a Mandala dataset to the multimedia community and further validate the application value of the proposed model.