Stroke-GAN Painter: Learning to paint artworks using stroke-style generative adversarial networks

Qian Wang, Cai Guo, Hong-Ning Dai*, Ping Li*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)


It is a challenging task to teach machines to paint like human artists in a stroke-by-stroke fashion. Despite advances in stroke-based image rendering and deep learning-based image rendering, existing painting methods have limitations: they (i) lack flexibility to choose different art-style strokes, (ii) lose content details of images, and (iii) generate few artistic styles for paintings. In this paper, we propose a stroke-style generative adversarial network, called Stroke-GAN, to solve the first two limitations. Stroke-GAN learns styles of strokes from different stroke-style datasets, so can produce diverse stroke styles. We design three players in Stroke-GAN to generate pure-color strokes close to human artists’ strokes, thereby improving the quality of painted details. To overcome the third limitation, we have devised a neural network named Stroke-GAN Painter, based on Stroke-GAN; it can generate different artistic styles of paintings. Experiments demonstrate that our artful painter can generate various styles of paintings while well-preserving content details (such as details of human faces and building textures) and retaining high fidelity to the input images. [Figure not available: see fulltext.].

Original languageEnglish
Pages (from-to)787-806
Number of pages20
JournalComputational Visual Media
Issue number4
Early online date11 Mar 2023
Publication statusPublished - Dec 2023

Scopus Subject Areas

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence

User-Defined Keywords

  • AI painting
  • painting strokes
  • artistic style


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