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 h...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.展开更多
基金This work was supported in part by the Hong Kong Institute of Business Studies(HKIBS)Research Seed Fund under Grant HKIBS RSF-212-004in part by The Hong Kong Polytechnic University under Grant P0030419,Grant P0030929,and Grant P0035358.
文摘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.
