Emergence of Painting Ability via Recognition-Driven Evolution

TL;DR

This paper presents a model that simulates the evolution of human-like painting abilities by optimizing strokes and color palettes for recognition accuracy, demonstrating improvements in artistic expression and image compression.

Contribution

The paper introduces a recognition-driven evolutionary model for painting that combines stroke and palette learning to enhance visual communication and artistic quality.

Findings

Achieves high recognition accuracy with minimal strokes and colors.

Outperforms traditional image compression methods.

Produces artistically expressive abstract sketches.

Abstract

From Paleolithic cave paintings to Impressionism, human painting has evolved to depict increasingly complex and detailed scenes, conveying more nuanced messages. This paper attempts to emerge this artistic capability by simulating the evolutionary pressures that enhance visual communication efficiency. Specifically, we present a model with a stroke branch and a palette branch that together simulate human-like painting. The palette branch learns a limited colour palette, while the stroke branch parameterises each stroke using B\'ezier curves to render an image, subsequently evaluated by a high-level recognition module. We quantify the efficiency of visual communication by measuring the recognition accuracy achieved with machine vision. The model then optimises the control points and colour choices for each stroke to maximise recognition accuracy with minimal strokes and colours. Experimental results show that our model achieves superior performance in high-level recognition tasks, delivering artistic expression and aesthetic appeal, especially in abstract sketches. Additionally, our approach shows promise as an efficient bit-level image compression technique, outperforming traditional methods.