Unified Visual Comparison Framework for Human and AI Paintings Using Neural Embeddings and Computational Aesthetics

Yilin Ye, Rong Huang, Kang Zhang, Wei Zeng

Abstract: To facilitate comparative analysis of artificial intelligence (AI) and human paintings, we present a unified computational framework combining neural embedding and computational aesthetic features. We first exploit CLIP embedding to provide a projected overview for human and AI painting datasets, and we next leverage computational aesthetic metrics to obtain explainable features of paintings. On that basis, we design a visual analytics system that involves distribution discrepancy measurement for quantifying dataset differences and evolutionary analysis for comparing artists with AI models. Case studies comparing three AI-generated datasets with three human paintings datasets, and analyzing the evolutionary differences between authentic Picasso paintings and AI-generated ones, show the effectiveness of our framework.