ChipGAN

Abstract

Style transfer has been successfully applied on photos to generate realistic western paintings. However, because of the inherently different painting techniques adopted by Chinese and western paintings, directly applying existing methods cannot generate satisfactory results for Chinese ink wash painting style transfer. This paper proposes ChipGAN, an end-to-end Generative Adversarial Network based architecture for photo to Chinese ink wash painting style transfer. The core modules of ChipGAN enforce three constraints -- voids, brush strokes, and ink wash tone and diffusion -- to address three key techniques commonly adopted in Chinese ink wash painting. We conduct stylization perceptual study to score the similarity of generated paintings to real paintings by consulting with professional artists based on the newly built Chinese ink wash photo and image dataset. The advantages in visual quality compared with state-of-the-art networks and high stylization perceptual study scores show the effectiveness of the proposed method.