@article{Wang2023, 
author = {Qian Wang and Cai Guo and Hong-Ning Dai and Ping Li},
title = {Stroke-GAN Painter: Learning to paint artworks using stroke-style generative adversarial networks},
year = {2023},
journal = {Computational Visual Media},
volume = {9},
number = {4},
pages = {787-806},
keywords = {AI painting, painting strokes, artistic style},
url = {https://www.sciopen.com/article/10.1007/s41095-022-0287-3},
doi = {10.1007/s41095-022-0287-3},
abstract = {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.}
}