Stroke-Based Stylization Learning and Rendering with Inverse Reinforcement Learning

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, we develop an AI-aided art authoring (A4) system of nonphotorealistic rendering that allows users to automatically generate brush stroke paintings in a specific artist’s style. Within the reinforcement learning framework of brush stroke generation proposed by Xie et al. [Xie et al. , 2012], our contribution in this paper is to learn artists’ drawing styles from video-captured stroke data by inverse reinforcement learning. Through experiments, we demonstrate that our system can successfully learn artists’ styles and render pictures with consistent and smooth brush strokes.

Authors

• Ning Xie University of Electronic Science and Technology of China, Chengdu, China
• Tingting Zhao Tianjin University of Science and Technology
• Feng Tian Institute of Software, Chinese Academy of Sciences University of Chinese Academy of Sciences
• Xiao Hua Zhang
• Masashi Sugiyama RIKEN Center for Advanced Intelligence Project The University of Tokyo

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