Author:
Wang Qian,Guo Cai,Dai Hong-Ning,Li Ping
Abstract
AbstractIt 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.
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition
Cited by
5 articles.
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