TwinGAN: Twin Generative Adversarial Network for Chinese Landscape Painting Style Transfer

Der Lor Way*, Chang Hao Lo, Yu Hsien Wei, Zen Chung Shih

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Recently, style transfers have received considerable attention. However, most of these studies were suitable for Western paintings. In this paper, a deep learning method is proposed to imitate multiple styles of Chinese landscape paintings. Twin generative adversarial network style transfer was proposed based on the characteristics of Chinese landscape ink paintings. SketchGAN and renderGAN were performed using generative models based on generative adversarial networks. The SketchGAN involves determining the structure and simplifying the content of an input image. RenderGAN involves transferring the results of sketchGAN into the final stylized image. Moreover, a loss function was designed to maintain the shape of the input content image. Finally, the proposed TwinGAN was successfully used to imitate five styles of Chinese landscape ink paintings. This study also provided ablation studies and comparisons with previous works. The experimental results show that our algorithm synthesizes Chinese landscape stylized paintings that are higher in quality than those produced by previous algorithms.

Original languageEnglish
Pages (from-to)60844-60852
Number of pages9
JournalIEEE Access
StatePublished - 2023


  • Chinese landscape painting
  • Deep neural networks
  • generative adversarial network (GAN)
  • loss function
  • style transfer


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