Based on StyleGAN for face image cartoon style transfer
Fenli Fu, Rui Li · 2023
Face cartoon style transfer to generate high quality face images has become an art form pursued by many people, but face style transfer generally has problems of incomplete detail information after migration and poor generation quality in some exaggerated styles. In this paper, By proposing a network model based on StyleGAN suitable for face style transfer by improving the StyleGAN generator part, introducing a style restriction module to characterize the color as well as more detailed information, and the overall process uses a progressive image generation strategy to gradually generate highquality style transfer result maps. The results show that the method can not only achieve style transfer in both domains, but also reconstruct low-resolution images to better characterize their features with better visual effects.