2019/06/17 by Biao Jia, Jia, Biao, Jonathan Brandt +9 · 4 citations
Computer Science · #Action (physics) #Advanced Vision and Imaging #Artificial Intelligence (cs.AI) #Artificial intelligence #Brush #Computer Graphics and Visualization Techniques #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #ENCODE #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Image (mathematics) #Machine Learning (cs.LG) #Machine learning #Reinforcement learning #Scratch #Space (punctuation) #cs.AI #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.1906.06841
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2019/06/17 · arxiv created 2019/09/21 · arxiv updated 2019/09/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/08
We present a novel reinforcement learning-based natural media painting algorithm. Our goal is to reproduce a reference image using brush strokes and we encode the objective through observations. Our formulation takes into account that the distribution of the reward in the action space is sparse and training a reinforcement learning algorithm from scratch can be difficult. We present an approach that combines self-supervised learning and reinforcement learning to effectively transfer negative samples into positive ones and change the reward distribution. We demonstrate the benefits of our painting agent to reproduce reference images with brush strokes. The training phase takes about one hour and the runtime algorithm takes about 30 seconds on a GTX1080 GPU reproducing a 1000x800 image with 20,000 strokes.