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arXiv · 1706.03319

Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN

Lvmin Zhang·Yi Ji·Xin Lin
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Abstract

Recently, with the revolutionary neural style transferring methods, creditable paintings can be synthesized automatically from content images and style images. However, when it comes to the task of applying a painting's style to an anime sketch, these methods will just randomly colorize sketch lines as outputs and fail in the main task: specific style tranfer. In this paper, we integrated residual U-net to apply the style to the gray-scale sketch with auxiliary classifier generative adversarial network (AC-GAN). The whole process is automatic and fast, and the results are creditable in the quality of art style as well as colorization.

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Lvmin Zhang, Yi Ji, Xin Lin. 2017-06-11. Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN. https://arxiv.org/abs/1706.03319

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