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

Ning XIE  Hirotaka HACHIYA  Masashi SUGIYAMA  

IEICE TRANSACTIONS on Information and Systems   Vol.E96-D   No.5   pp.1134-1144
Publication Date: 2013/05/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E96.D.1134
Print ISSN: 0916-8532
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
painterly rendering,  stroke-based rendering,  reinforcement learning,  policy gradient,  

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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.