发表机构
University of Bucharest（布加勒斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究更新的生成模型模仿当代艺术作品的能力及不同语言模型中风格评估的多维度一致性。利用五个计算机视觉模型，通过余弦距离捕捉多种特征，对比新旧模型。结果显示新模型语义对齐改进、多样性增加，但浅层特征表现稍弱，还通过艺术家反馈进行情境化。
AI 中文摘要
本文旨在实现两个目标。其一，研究更新的生成模型在模仿当代艺术作品方面是否更出色。其二，探索不同语言模型中等式评估的多维度性质的一致性。基于先前工作，我们分析了人工智能生成的模仿作品与十二位当代艺术家的原创作品之间的风格相似性。我们使用了五个互补的计算机视觉模型，通过高维嵌入空间中的余弦距离来捕捉纹理、颜色、语义、构图和感知特征。结果表明，我们使用的更新的图像生成模型产生的模仿作品在语义对齐方面有所改进，并且比先前工作中使用的模型具有更大的多样性。然而，在颜色、纹理和感知一致性等浅层特征方面表现略逊一筹。我们的研究结果证实艺术风格本质上是多维度的，对其进行衡量并不依赖于任何空间架构。这些定量结果通过来自人类评估者（即艺术家本人）的反馈得以情境化。
英文摘要
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.
