ChinaStyle: A Mask-Aware Generative Adversarial Network for Chinese Traditional Image Translation

Abstract

GANs make it effective to generate artworks using appropriate collections. However, most training dataset either contain paintings that were only from one artist or contain only one category. There is few training datasets for Chinese traditional figure paintings. This paper presents a new high-quality dataset named ChinaStyle Dataset including six categories, containing 1913 images totally. We further proposes Mask-Aware Generative Adversarial Networks (MA-GAN) to transfer realistic portraiture to different styles of Chinese paintings. Different from existing mothed, MA-GAN uses a single model only once with our unpaired dataset. Besides, Mask-aware strategy is used to generate free-hand style of Chinese paintings. In addition, a color preserved loss is proposed to alleviate the color free problem. Experimental results and user study demonstrate that MA-GAN achieves a natural and competitive performance compared with existing methods.