Portrait artistic style transfer aims to transfer the style from a given reference artistic portrait painting to a portrait photo while preserving the basic semantic structure of the person's face. However, due to the sensitivity of the human visual system to the facial structure of person, the task of artistic style transfer of portraits is often more challenging than that for general image, especially for caricature type which with more abstract style elements. Existing image style transfer methods, which do not consider the abstraction of the caricature style and the preservation of basic semantic structure of the portrait face, often suffer from serious structural collapse and feature information confusion when applied to the portrait caricature task. To address this problem, this paper proposed a double-stream cycle mapping DSCM(double-stream cycle mapping network) network to portrait caricature. Firstly, based on BeautyGAN, it introduced a structural consistency loss and cooperating with the cycle consistency loss to maintain the integrity of the overall semantic structure of the portrait. Secondly, it designed a feature encoder combined with U²-Net to capture more valuable feature information of input images at different scales. In addition, it further introduced a style discriminator to discriminate the encoded style features to assist the network in learning abstract caricature style features closer to the target image. The experiments conducted qualitative comparisons of five advanced methods, and quantitative comparisons of FID(Fréchet inception distance) and PSNR(peak signal to noise ratio) index scores. The experimental results show that this method is superior to other methods. Through extensive experimental verification, the portrait caricature obtained by this method not only maintains the overall structure of the portrait and the basic semantic structure of the face, but also fully learns the abstract style of caricature.

[2023-08-14] Accepted Paper /doc/2023.05.0226/v1

[2023-12-05] Printed Article /doc/2023.05.0226/v2