The Art That Poses Back: Assessing AI Pastiches after Contemporary Artworks

• URL: http://arxiv.org/abs/2603.06324v1
• Date: Fri, 06 Mar 2026 14:25:55 GMT
• Title: The Art That Poses Back: Assessing AI Pastiches after Contemporary Artworks
• Abstract summary: This study explores artificial visual creativity, focusing on ChatGPT's ability to generate new images intentionally pastiching original artworks such as paintings, drawings, sculptures and installations.<n>The process involved twelve artists from Romania, Bulgaria, France, Austria, and the United Kingdom, each invited to contribute with three of their artworks and to grade and comment on the AI-generated versions.<n>The analysis combines human evaluation with computational methods aimed at detecting visual and stylistic similarities or divergences between the original works and their AI-produced renditions.
• Score: 0.8566349439189714
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study explores artificial visual creativity, focusing on ChatGPT's ability to generate new images intentionally pastiching original artworks such as paintings, drawings, sculptures and installations. The process involved twelve artists from Romania, Bulgaria, France, Austria, and the United Kingdom, each invited to contribute with three of their artworks and to grade and comment on the AI-generated versions. The analysis combines human evaluation with computational methods aimed at detecting visual and stylistic similarities or divergences between the original works and their AI-produced renditions. The results point to a significant gap between color and texture-based similarity and compositional, conceptual, and perceptual one. Consequently, we advocate for the use of a "style transfer dashboard" of complementary metrics to evaluate the similarity between pastiches and originals, rather than using a single style metric. The artists' comments revealed limitations of ChatGPT's pastiches after contemporary artworks, which were perceived by the authors of the originals as lacking dimensionality, context, and intentional sense, and seeming more of a paraphrase or an approximate quotation rather than as a valuable, emotion-evoking artwork.

Related papers

• Conducting Stylistic Analysis of Paintings through an Art-History Agent [0.0]
  We present an AI framework that automates stylistic analysis of paintings.<n>By training a vision transformer (ViT) on a large corpus of paintings with metadata, our system encodes this art history-specific data as embeddings.<n>A large language model (LLM) then interprets each feature by retrieving associated artworks and their accompanying curator-written texts, and synthesizes them into descriptions.
  arXiv  Detail & Related papers  (2026-08-30T08:10:58Z)
• Back to Back with a Copy: A Computational Analysis of AI-Generated Visual Contemporary Art Pastiches [0.7984696597255688]
  This paper investigates whether newer generative models are getting better at pastiching contemporary artworks.<n>We analyze stylistic similarity between AI generated pastiches and the original artworks of twelve contemporary artists.
  arXiv  Detail & Related papers  (2026-07-22T13:30:07Z)
• Does AI See like Art Historians? Interpreting How Vision Language Models Recognize Artistic Style [60.562013130566726]
  We characterize the mechanisms underlying VLMs' ability to predict artistic style.<n>We employ a latent-space decomposition approach to identify concepts that drive art style prediction.<n>In cases where an irrelevant concept was used to successfully predict style, art historians identified possible reasons for its success.
  arXiv  Detail & Related papers  (2026-03-11T17:49:45Z)
• ArtistAuditor: Auditing Artist Style Pirate in Text-to-Image Generation Models [61.55816738318699]
  We propose a novel method for data-use auditing in the text-to-image generation model.<n>ArtistAuditor employs a style extractor to obtain the multi-granularity style representations and treats artworks as samplings of an artist's style.<n>The experimental results on six combinations of models and datasets show that ArtistAuditor can achieve high AUC values.
  arXiv  Detail & Related papers  (2025-04-17T16:15:38Z)
• Compose Your Aesthetics: Empowering Text-to-Image Models with the Principles of Art [61.28133495240179]
  We propose a novel task of aesthetics alignment which seeks to align user-specified aesthetics with the T2I generation output.<n>Inspired by how artworks provide an invaluable perspective to approach aesthetics, we codify visual aesthetics using the compositional framework artists employ.<n>We demonstrate that T2I DMs can effectively offer 10 compositional controls through user-specified PoA conditions.
  arXiv  Detail & Related papers  (2025-03-15T06:58:09Z)
• Learning Artistic Signatures: Symmetry Discovery and Style Transfer [8.288443063900825]
  There is no undisputed definition of artistic style.<n>Style should be thought of as a set of global symmetries that dictate the arrangement of local textures.<n>We show that by considering both local and global features, using both Lie generators and traditional measures of texture, we can quantitatively capture the stylistic similarity between artists better than with either set of features alone.
  arXiv  Detail & Related papers  (2024-12-05T18:56:23Z)
• Have Large Vision-Language Models Mastered Art History? [3.790400719319821]
  We test whether Vision-Language Models (VLMs) can classify the style, author and creation date of paintings.<n>Art historians have long studied the unique aspects of artworks, with style prediction being a crucial component of their discipline.
  arXiv  Detail & Related papers  (2024-09-05T13:33:57Z)
• Rethinking Artistic Copyright Infringements in the Era of Text-to-Image Generative Models [47.19481598385283]
  ArtSavant is a tool to determine the unique style of an artist by comparing it to a reference dataset of works from WikiArt. We then perform a large-scale empirical study to provide quantitative insight on the prevalence of artistic style copying across 3 popular text-to-image generative models.
  arXiv  Detail & Related papers  (2024-04-11T17:59:43Z)
• Learning to Evaluate the Artness of AI-generated Images [64.48229009396186]
  ArtScore is a metric designed to evaluate the degree to which an image resembles authentic artworks by artists. We employ pre-trained models for photo and artwork generation, resulting in a series of mixed models. This dataset is then employed to train a neural network that learns to estimate quantized artness levels of arbitrary images.
  arXiv  Detail & Related papers  (2023-05-08T17:58:27Z)
• Inversion-Based Style Transfer with Diffusion Models [78.93863016223858]
  Previous arbitrary example-guided artistic image generation methods often fail to control shape changes or convey elements. We propose an inversion-based style transfer method (InST), which can efficiently and accurately learn the key information of an image.
  arXiv  Detail & Related papers  (2022-11-23T18:44:25Z)
• Paint by Word [32.05329583044764]
  We investigate the problem of zero-shot semantic image painting. Instead of painting modifications into an image using only concrete colors or a finite set of semantic concepts, we ask how to create semantic paint based on open full-text descriptions. Our method combines a state-of-the art generative model of realistic images with a state-of-the-art text-image semantic similarity network.
  arXiv  Detail & Related papers  (2021-03-19T17:59:08Z)
• Identifying centres of interest in paintings using alignment and edge detection: Case studies on works by Luc Tuymans [1.8855270809505869]
  We set the first preliminary steps to algorithmically deconstruct some of the transformations that an artist applies to an original image in order to establish centres of interest. We introduce a comparative methodology that first cuts out the minimal segment from the original image on which the painting is based, then aligns the painting with this source, investigates micro-differences to identify centres of interest and attempts to understand their role.
  arXiv  Detail & Related papers  (2021-01-04T10:04:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.