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- XIE Ning
XIE Ning
Center for Future Media, University of Electronic Science and Technology of China, Chengdu 611731, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
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Search for this author on this site - ZHAO Ting-Ting
ZHAO Ting-Ting
School of Computer Science and Information Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
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Search for this author on this site - YANG Yang
YANG Yang
Center for Future Media, University of Electronic Science and Technology of China, Chengdu 611731, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
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Search for this author on this site - WEI Qin
WEI Qin
Guizhou Provincial Key Laboratory of Public Big Data(Guizhou University), Guiyang 550025, China
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Search for this author on this site - Heng Tao SHEN
Heng Tao SHEN
Center for Future Media, University of Electronic Science and Technology of China, Chengdu 611731, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
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National Natural Science Foundation of China (61602088, 61572108, 61632007); Fundamental Research Funds for the Central Universities (ZYGX2016J212, ZYGX2014Z007, ZYGX2015J055); National Basic Research Program of China (973) (2015CB 856000)
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Among various traditional art forms, brush stroke drawing is one of the widely used styles in modern computer graphic tools such as GIMP, Photoshop and Painter. In this paper, an AI-aided art authoring (A4) system of non-photorealistic rendering is developed that allows users to automatically generate brush stroke paintings in a specific artist's style. Within the reinforcement learning framework of brush stroke generation, the first contribution in this paper is the application of regularized policy gradient method, which is more suitable for the stroke generation task. The other contribution is to learn artists' drawing styles from video-captured stroke data by inverse reinforcement learning. Experiments demonstrate that the presented system can successfully learn artists' styles and render pictures with consistent and smooth brush strokes.
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谢宁,赵婷婷,杨阳,魏琴,Heng Tao SHEN.基于创意序列学习的艺术风格学习与绘制系统.软件学报,2018,29(4):1071-1084
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History
- Received:May 01,2017
- Revised:June 26,2017
- Online: November 29,2017
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
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