LEARNING TO DRAW THROUGH A MULTI-STAGE ENVIRONMENT MODEL BASED REINFORCEMENT LEARNING

Machine drawing has gradually become a hot research topic in computer vision and robotics domains recently. However, decomposing a given target image from raster space into an ordered sequence and reconstructing those strokes is a challenging task. In this work, we focus on the drawing task for the images in various styles where the distribution of stroke parameters differs. We propose a multi-stage environment model based reinforcement learning (RL) drawing framework with fine-grained perceptual reward to guide the agent under this framework to draw details and an overall outline of the target image accurately. The experiments show that the visual quality of our method slightly outperforms SOTAmethod in nature and doodle style, while it outperforms the SOTA approaches by a large margin with high efficiency in sketch style.