Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting

• XIE Ning

  Tokyo Institute of Technology

• HACHIYA Hirotaka

  Tokyo Institute of Technology

• SUGIYAMA Masashi

  Tokyo Institute of Technology

Abstract

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.

Journal

• IEICE Transactions on Information and Systems

  IEICE Transactions on Information and Systems E96.D (5), 1134-1144, 2013

  The Institute of Electronics, Information and Communication Engineers

Keywords

• painterly rendering
• stroke-based rendering
• reinforcement learning
• policy gradient

Details

• CRID: 1390001204379437696
• NII Article ID: 10031193966
• NII Book ID: AA10826272
• DOI: 10.1587/transinf.e96.d.1134
• ISSN: 17451361
• ISSN: 09168532
• Web Site: https://www.jstage.jst.go.jp/article/transinf/E96.D/5/E96.D_1134/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed