Artist Agent A2: Stroke Painterly Rendering Based on Reinforcement Learning
スポンサーリンク
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
-
Masashi Sugiyama
Department Of Computer Science Tokyo Institute Of Technology
-
Hirotaka Hachiya
Department Of Computer Science Tokyo Institute Of Technology
-
Ning Xie
Department Of Computer Science Tokyo Institute Of Technology
関連論文
- Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation
- Artist Agent A2: Stroke Painterly Rendering Based on Reinforcement Learning
- Canonical Dependency Analysis based on Squared-loss Mutual Information
- Output Divergence Criterion for Active Learning in Collaborative Settings
- Personal Style Learning in Sumi-e Stroke-based Rendering by Inverse Reinforcement Learning
- Personal Style Learning in Sumi-e Stroke-based Rendering by Inverse Reinforcement Learning