Artist agent A[2]: stroke painterly rendering based on reinforcement learning (情報論的学習理論と機械学習)
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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 appealing painting styles that has attracted artists around the world. The major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically find such strokes, we propose to model the brush as a reinforcement-learning (RL) agent, and learn desired brush-trajectories by maximizing the sum of rewards in the policy search framework. We also provide elaborate design of state space, action space, and a reward function tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through simulated Sumi-e experiments.
- 2011-08-29
著者
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Hachiya Hirotaka
Department Of Computer Science Tokyo Institute Of Technology
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Sugiyama Masashi
Tokyo Inst. Of Technol.
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Sugiyama Masashi
Department Of Computer Science Tokyo Institute Of Technology
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Hachiya Hirotaka
Tokyo Inst. Of Technol.
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Xie Ning
Department Of Computer Science Tokyo Institute Of Technology
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Sugiyama Masashi
Department Of Chemistry Faculty Of Science Tokyo University Of Science
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Sugiyama Masashi
Department of Applied Chemistry, Yamanashi University
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