[1910.05253] Adversarial Colorization Of Icons Based On Structure And Color Conditionsopen searchopen navigation menucontact arXivarXiv Twitter

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 previo

3 mentions: @hardmaru@ak92501@ankurhandos
Date: 2020/05/19 17:21

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@hardmaru Neural network trained to colorize icons in various styles t.co/OsMS2RRPtF t.co/kSwINoUymq
@ak92501 Adversarial Colorization Of Icons Based On Structure And Color Conditions pdf: t.co/6tIoJZiXye abs: t.co/2LakM2d1bk github: t.co/hV7v3wlzvU t.co/OQ550tvGgp

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