Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings

Abstract

Generation of stroke-based non-photorealistic imagery, is an important problem in the computer vision community. As an endeavor in this direction, substantial recent research efforts have been focused on teaching machines "how to paint", in a manner similar to a human painter. However, the applicability of previous methods has been limited to datasets with little variation in position, scale and saliency of the foreground object. As a consequence, we find that these methods struggle to cover the granularity and diversity possessed by real world images. To this end, we propose a Semantic Guidance pipeline with 1) a bi-level painting procedure for learning the distinction between foreground and background brush strokes at training time. 2) We also introduce invariance to the position and scale of the foreground object through a neural alignment model, which combines object localization and spatial transformer networks in an end to end manner, to zoom into a particular semantic instance. 3) The distinguishing features of the in-focus object are then amplified by maximizing a novel guided backpropagation based focus reward. The proposed agent does not require any supervision on human stroke-data and successfully handles variations in foreground object attributes, thus, producing much higher quality canvases for the CUB-200 Birds and Stanford Cars-196 datasets. Finally, we demonstrate the further efficacy of our method on complex datasets with multiple foreground object instances by evaluating an extension of our method on the challenging Virtual-KITTI dataset. Source code and models are available at https://github.com/1jsingh/semantic-guidance.

References in corpus (12)

• Continuous control with deep reinforcement learning
• Rethinking Atrous Convolution for Semantic Image Segmentation
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• Striving for Simplicity: The All Convolutional Net
• A Neural Representation of Sketch Drawings
• Synthesizing Programs for Images using Reinforced Adversarial Learning
• Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting
• A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
• Learning to Paint With Model-based Deep Reinforcement Learning
• Neural Painters: A learned differentiable constraint for generating brushstroke paintings
• Unsupervised Doodling and Painting with Improved SPIRAL
• Unsupervised Image to Sequence Translation with Canvas-Drawer Networks

Cited by in corpus (1)

• Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges