Related papers: The persistence of painting styles

The persistence of painting styles

URL: http://arxiv.org/abs/2511.16695v1
Date: Mon, 17 Nov 2025 13:25:04 GMT
Title: The persistence of painting styles
Authors: Reetikaa Reddy Munnangi, Barbara Giunti,
Abstract summary: We show how persistent homology (PH), a method from topological data analysis, provides objective and interpretable insights on artistic styles.<n>We show how PH can, with statistical certainty, differentiate between artists, both from different artistic currents and from the same one, and distinguish images of an artist from an AI-generated image in the artist's style.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Art is a deeply personal and expressive medium, where each artist brings their own style, technique, and cultural background into their work. Traditionally, identifying artistic styles has been the job of art historians or critics, relying on visual intuition and experience. However, with the advancement of mathematical tools, we can explore art through more structured lens. In this work, we show how persistent homology (PH), a method from topological data analysis, provides objective and interpretable insights on artistic styles. We show how PH can, with statistical certainty, differentiate between artists, both from different artistic currents and from the same one, and distinguish images of an artist from an AI-generated image in the artist's style.

Related papers

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)
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)
Opt-In Art: Learning Art Styles Only from Few Examples [50.60063523054282]
We show that it is possible to adapt a model trained without paintings to an artistic style, given only few examples.<n>Surprisingly, our findings suggest that high-quality artistic outputs can be achieved without prior exposure to artistic data.
arXiv Detail & Related papers (2024-11-29T18:59:01Z)
GalleryGPT: Analyzing Paintings with Large Multimodal Models [64.98398357569765]
Artwork analysis is important and fundamental skill for art appreciation, which could enrich personal aesthetic sensibility and facilitate the critical thinking ability. Previous works for automatically analyzing artworks mainly focus on classification, retrieval, and other simple tasks, which is far from the goal of AI. We introduce a superior large multimodal model for painting analysis composing, dubbed GalleryGPT, which is slightly modified and fine-tuned based on LLaVA architecture.
arXiv Detail & Related papers (2024-08-01T11:52:56Z)
AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art [36.21731898719347]
We conduct a comprehensive analysis to position AI-generated art within the context of human art heritage. Our comparative analysis is based on an extensive dataset, dubbed ArtConstellation'' Key finding is that AI-generated artworks are visually related to the principle concepts for modern period art made in 1800-2000.
arXiv Detail & Related papers (2024-02-04T11:49:51Z)
Impressions: Understanding Visual Semiotics and Aesthetic Impact [66.40617566253404]
We present Impressions, a novel dataset through which to investigate the semiotics of images. We show that existing multimodal image captioning and conditional generation models struggle to simulate plausible human responses to images. This dataset significantly improves their ability to model impressions and aesthetic evaluations of images through fine-tuning and few-shot adaptation.
arXiv Detail & Related papers (2023-10-27T04:30:18Z)
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)
Not Only Generative Art: Stable Diffusion for Content-Style Disentanglement in Art Analysis [23.388338598125195]
GOYA is a method that distills the artistic knowledge captured in a recent generative model to disentangle content and style. Experiments show that synthetically generated images sufficiently serve as a proxy of the real distribution of artworks.
arXiv Detail & Related papers (2023-04-20T13:00:46Z)
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)

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