ChipGAN: A Generative Adversarial Network for Chinese Ink Wash Painting Style Transfer Bin He1, Feng Gao2, Daiqian Ma1;3, Boxin Shi1, Ling-Yu Duan1∗ National Engineering Lab for Video Technology, Peking University, Beijing, China1 The Future Lab, Tsinghua University, Beijing, China2 SECE of Shenzhen Graduate School, Peking University, Shenzhen, China3
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[email protected],{madaiqian,shiboxin,lingyu}@pku.edu.cn ABSTRACT Style transfer has been successfully applied on photos to gener- Gatys et al. ate realistic western paintings. However, because of the inherently Oli different painting techniques adopted by Chinese and western paint- C h ings, directly applying existing methods cannot generate satisfac- input photo I ip Generated Western Painting Real Western Painting nk G W A a N tory results for Chinese ink wash painting style transfer. This paper Gatys et al. Ink Wash sh 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 paint- ing. We conduct stylization perceptual study to score the similarity Generated Ink Wash Painting Generated Ink Wash Painting Real Chinese Painting of generated paintings to real paintings by consulting with pro- Figure 1: Given an input photo, existing style transfer tech- fessional artists based on the newly built Chinese ink wash photo nique (Gatys et al. [11]) is able to generate western paint- and image dataset. The advantages in visual quality compared with ing with visually close style to the real painting (top row), state-of-the-art networks and high stylization perceptual study but not for the Chinese ink wash painting (bottom left).