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

2012/06/18 by Ning Xie, Hirotaka Hachiya, Masashi Sugiyama · 1 voice · 3 citations
Computer Science · Engineering · Neuroscience · #3D Shape Modeling and Analysis #Aesthetic Perception and Analysis #Computer Graphics and Visualization Techniques #cs.GR #cs.LG #stat.ML

paper · pdf · doi:10.1587/transinf.e96.d.1134

openalex publication_date 2013/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• Stylized Neural Painting
• LPaintB: Learning to Paint from Self-Supervision
• Learning to Paint With Model-based Deep Reinforcement Learning

Discussions

• Artist Agent: A RL Approach to Stroke Generation in Oriental Ink Painting [hn, 4 points, 0 comments]

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