  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.
