Adversarial colorization of icons based on structure and color conditions

Tsai Ho Sun, Chien Hsun Lai, Sai-Keung Wong, Yu-Shuen Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publicationMM 2019 - Proceedings of the 27th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery, Inc
Pages683-691
Number of pages9
ISBN (Electronic)9781450368896
DOIs
StatePublished - Oct 2019
Event27th ACM International Conference on Multimedia, MM 2019 - Nice, France
Duration: 21 Oct 201925 Oct 2019

Publication series

NameMM 2019 - Proceedings of the 27th ACM International Conference on Multimedia

Conference

Conference27th ACM International Conference on Multimedia, MM 2019
CountryFrance
CityNice
Period21/10/1925/10/19

Keywords

  • Colorization
  • Generative adversarial networks
  • Icon

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