Emergence of Painting Ability via Recognition-Driven Evolution

2025-01-09

AI Summary

This paper addresses the dual limitations of low efficiency and weak semantic expressiveness in machine painting. We propose a recognition-driven, dual-path co-evolutionary painting model. Methodologically, it comprises a stroke branch—parameterized via Bézier curves—and a palette branch—comprising a learnable, finite color set—jointly optimized to maximize high-level visual recognition accuracy, while simultaneously refining stroke count, control point positions, and color selection. Our key contribution is the first formulation of painting capability as an unsupervised, recognition-guided co-evolutionary process, enabling spontaneous emergence of abstraction and aesthetic qualities. Moreover, the model unifies high-fidelity abstract sketch generation with bit-level image compression. Experiments demonstrate significant improvements over baselines in recognition performance, while also surpassing conventional coding schemes in both abstraction fidelity and compression efficiency.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Machine Learning in Art
• Optimized Image Compression
• Abstract Art Generation

Innovation

• Color and Stroke Optimization
• Bezier Curve Control
• Abstract Art Recognition