用五种视觉模型分析AI模仿当代艺术的水平与风格一致性
Back to Back with a Copy: A Computational Analysis of AI-Generated Visual Contemporary Art Pastiches
- 用五种计算机视觉模型从纹理、色彩、语义等维度量化风格相似性
- 新模型生成作品在语义匹配上更优，多样性更高，但色彩纹理稍弱
- 艺术家亲测反馈验证了多维风格评估的有效性，适合艺术与AI交叉研究者
本文旨在双重探究：一是评估新一代生成模型在模仿当代艺术作品方面的进步；二是考察不同大语言模型对风格评价的多维一致性。基于前期工作，我们分析了12位当代艺术家作品与相应AI生成赝品之间的风格相似性。采用五种互补的计算机视觉模型，通过高维嵌入空间中的余弦距离，捕捉纹理、色彩、语义、构图和感知特征。结果显示，所用新图像生成模型产生的赝品在语义对齐上表现更优，且多样性更高，但浅层特征（如色彩、纹理、感知契合度）略逊于前代模型。研究证实艺术风格本质上是多维的，其测量不依赖特定空间结构。定量结果结合了艺术家本人的人类评估反馈，增强了结论的可信度。
原文摘要 · Abstract (English)
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.
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