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.
Abstract (translated)
这项研究探讨了人工智能的视觉创造力，重点关注ChatGPT生成新图像的能力，这些新图像是对原作如绘画、素描、雕塑和装置艺术进行有意模仿的结果。该过程邀请来自罗马尼亚、保加利亚、法国、奥地利和英国的十二位艺术家参与，每位艺术家贡献三件自己的作品，并对其AI生成版本进行评分和评论。分析结合了人类评价与计算方法，旨在检测原始作品与其人工智能生成版本之间的视觉和风格上的相似性或差异。 研究结果表明，在颜色和纹理基础上的相似性和构成、概念及感知层面的相似性之间存在显著差距。因此，我们倡导使用一个“风格转换仪表板”，采用互补指标来评估模仿作品与原作之间的相似度，而不是依赖单一的风格指标。 艺术家们的评论揭示了ChatGPT在对当代艺术品进行模仿时存在的局限性。原作者认为这些模仿缺乏深度、背景信息和有意图的感觉，并且看起来更像是引用或近似复制，而非具有情感价值的艺术品。
URL
https://arxiv.org/abs/2603.06324
