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

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.

Submitted to ACPR 2017

References in corpus (4)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Conditional Generative Adversarial Nets
• Going Deeper with Convolutions
• StyleBank: An Explicit Representation for Neural Image Style Transfer

Cited by in corpus (6)

• DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition
• Language-Driven Image Style Transfer
• Adversarial Colorization Of Icons Based On Structure And Color Conditions
• SLGAN: Style- and Latent-guided Generative Adversarial Network for Desirable Makeup Transfer and Removal
• Focusing on Persons: Colorizing Old Images Learning from Modern Historical Movies
• Face Attribute Invertion