{"title":"Unified Visual Comparison Framework for Human and AI Paintings using Neural Embeddings and Computational Aesthetics.","authors":"Yilin Ye, Rong Huang, Kang Zhang, Wei Zeng","doi":"10.1109/MCG.2025.3555122","DOIUrl":null,"url":null,"abstract":"<p><p>To facilitate comparative analysis of 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 the 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.</p>","PeriodicalId":55026,"journal":{"name":"IEEE Computer Graphics and Applications","volume":"PP ","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Computer Graphics and Applications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1109/MCG.2025.3555122","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
To facilitate comparative analysis of 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 the 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.
期刊介绍:
IEEE Computer Graphics and Applications (CG&A) bridges the theory and practice of computer graphics, visualization, virtual and augmented reality, and HCI. From specific algorithms to full system implementations, CG&A offers a unique combination of peer-reviewed feature articles and informal departments. Theme issues guest edited by leading researchers in their fields track the latest developments and trends in computer-generated graphical content, while tutorials and surveys provide a broad overview of interesting and timely topics. Regular departments further explore the core areas of graphics as well as extend into topics such as usability, education, history, and opinion. Each issue, the story of our cover focuses on creative applications of the technology by an artist or designer. Published six times a year, CG&A is indispensable reading for people working at the leading edge of computer-generated graphics technology and its applications in everything from business to the arts.