Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting
This addresses a domain-specific challenge in computer-based art simulation for artists and researchers, but appears incremental as it applies reinforcement learning to a known bottleneck in stroke generation.
The paper tackles the problem of automatically generating smooth and natural brush strokes in Oriental ink painting (Sumi-e) by modeling the brush as a reinforcement learning agent, and demonstrates its effectiveness through simulated experiments.
Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. 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 agent, and learn desired brush-trajectories by maximizing the sum of rewards in the policy search framework. We also provide elaborate design of actions, states, and rewards tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through simulated Sumi-e experiments.