Adversarial Colorization Of Icons Based On Structure And Color Conditions

TL;DR

This paper introduces a dual conditional GAN system that automatically colorizes icon contours based on structure and color style conditions, aiding designers and reducing their workload.

Contribution

The novel dual conditional GAN effectively combines contour and color style conditions for icon colorization, outperforming existing methods.

Findings

The system successfully colorizes icons according to specified conditions.

It outperforms several state-of-the-art techniques in icon colorization.

The approach reduces manual effort for designers.

Abstract

We present a system to help designers create icons that are widely used in banners, signboards, billboards, homepages, and mobile apps. Designers are tasked with drawing contours, whereas our system colorizes contours in different styles. This goal is achieved by training a dual conditional generative adversarial network (GAN) on our collected icon dataset. One condition requires the generated image and the drawn contour to possess a similar contour, while the other anticipates the image and the referenced icon to be similar in color style. Accordingly, the generator takes a contour image and a man-made icon image to colorize the contour, and then the discriminators determine whether the result fulfills the two conditions. The trained network is able to colorize icons demanded by designers and greatly reduces their workload. For the evaluation, we compared our dual conditional GAN to several state-of-the-art techniques. Experiment results demonstrate that our network is over the previous networks. Finally, we will provide the source code, icon dataset, and trained network for public use.