Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting
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概要
- 論文の詳細を見る
Oriental ink painting, called Sumi-e, is one of the most distinctive painting styles and has attracted artists around the world. Major challenges in Sumi-e simulation are to abstract complex scene information and reproduce smooth and natural brush strokes. To automatically generate such strokes, we propose to model the brush as a reinforcement learning agent, and let the agent learn the desired brush-trajectories by maximizing the sum of rewards in the policy search framework. To achieve better performance, we provide elaborate design of actions, states, and rewards specifically tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through experiments on Sumi-e simulation.
著者
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Sugiyama Masashi
Tokyo Inst. Of Technol.
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Hachiya Hirotaka
Tokyo Inst. Of Technol.
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XIE Ning
Tokyo Institute of Technology
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