DunhuangGAN: A Generative Adversarial Network for Dunhuang Mural Art Style Transfer

Style transfer has been successfully applied to various visual art creation tasks. However, due to the inherent style of Dunhuang mural art, existing methods can not produce high-quality mural art stylized images. We propose a novel model of DunhuangGAN for Dunhuang mural art style transfer. DunhuangGAN is based on the improved contrastive learning framework and optimized under the proposed multiple loss. Firstly, we propose a content-biased contrastive loss to alleviate the negative impacts caused by the inter-domain style differences. Secondly, we propose the line loss and color loss to simulate the line drawing modeling and heavy color of Dunhuang mural art. In addition, we introduce semantic loss to improve the visual effect of certain content element areas in generated images that are rare in the Dunhuang murals. Extensive experiments based on the collected dataset show that our method outperforms existing methods in the Dunhuang mural art style transfer task.