Ink painting style transfer using asymmetric cycle-consistent GAN

Abstract

Chinese ink painting, an artistic and cultural treasure, necessitates automatic generation for its preservation and evolution. We've innovatively observed and validated that the domain information between photographs and ink paintings, is asymmetrical, which has been overlooked by current style transfer algorithms. We propose an innovative generative adversarial network featuring an asymmetric cyclic consistency structure to address this in ink painting style transfer. This structure uses generators of differing capabilities to align with the asymmetry in transformation directions, improving image quality and model optimization speed. Additionally, we introduce two unique loss functions within the network. The salient edge loss intensifies the subject in the real photo and enhances the edge stroke of the drawn subject, a distinct attribute of ink painting. The feature-wise cycle consistency loss is designed to speed up model optimization. We've also built a Chinese bird ink painting dataset to validate effectiveness of the model. Extensive experiments on this and a public dataset demonstrate that our algorithm can comprehensively learn various stylistic features of ink painting, especially regarding brushstroke style, ink diffusion, and detail preservation. Furthermore, the quantitative results indicate our approach achieves superior results in generation quality and model efficiency compared to existing methods. For instance, compared to the most recent style transfer method, our method achieves an average decrease of 9.44% and 25.32% for FID and KID metrics across three datasets and reduces training and inference time costs by 46.81% and 22.71% respectively.