Back to Back with a Copy: A Computational Analysis of AI-Generated Visual Contemporary Art Pastiches

2026-07-22

AI Summary

This study evaluates the capacity of next-generation generative models to emulate contemporary artistic styles through a multidimensional analysis. Leveraging five complementary computer vision models, the authors compute cosine distances in high-dimensional embedding spaces between AI-generated images and original works by twelve contemporary artists across multiple stylistic dimensions—texture, color, semantics, composition, and perceptual features. For the first time, this quantitative assessment is integrated with subjective feedback from the artists themselves. The findings confirm that the multidimensional nature of artistic style is invariant across different embedding spaces. Moreover, while newer models demonstrate marked improvements in semantic alignment and output diversity compared to their predecessors, they exhibit slight regressions in capturing low-level characteristics such as color fidelity and textural detail.

Abstract

The aim of this paper is twofold. First, it investigates whether newer generative models are getting better at pastiching contemporary artworks. Second, it explores the consistency of the multidimensional nature of stylistic evaluation across different LLMs. Building on previous work, we analyze stylistic similarity between AI generated pastiches and the original artworks of twelve contemporary artists. We used five complementary computer vision models to capture texture, color, semantics, composition, and perceptual features through cosine distance in high-dimensional embedding spaces. The distances obtained show that the newer image generation model that we used has produced pastiches with improved semantic alignment and greater diversity than the model used in previous work. However, it was slightly less performant on shallow features such as color, texture, and perceptual adherence. Our findings confirm that artistic style is inherently multidimensional, and measuring it does not depend on any spatial architecture. These quantitative findings are contextualized through feedback from human evaluators, which are the artists themselves.

Problem

• AI-generated art
• style pastiche
• stylistic evaluation
• contemporary art
• generative models

Innovation

• stylistic similarity
• generative models
• multidimensional style evaluation
• computer vision embeddings
• AI-generated art