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- Title
Stroke-GAN Painter: Learning to paint artworks using stroke-style generative adversarial networks.
- Authors
Wang, Qian; Guo, Cai; Dai, Hong-Ning; Li, Ping
- 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.
- Subjects
GENERATIVE adversarial networks; DEEP learning; ARTISTIC style; PAINT; PAINTING techniques; PAINTERS; ACRYLIC paint
- Publication
Computational Visual Media, 2023, Vol 9, Issue 4, p787
- ISSN
2096-0433
- Publication type
Article
- DOI
10.1007/s41095-022-0287-3