用AI分析500年西方绘画，发现社会背景比色彩更关键
Context-aware Multimodal AI Reveals Hidden Pathways in Five Centuries of Art Evolution
- 用Stable Diffusion提取画作形式与语境双维度信息
- 语境特征比色彩等形式元素更能区分艺术流派与时代
- 可复现艺术演变轨迹，适合研究艺术史与社会变迁者
生成式AI正重塑科技与艺术的交汇点，为大规模艺术作品分析提供新视角。尽管其创作能力广受关注，但其在隐空间表征艺术作品方面的潜力仍待挖掘。本研究利用Stable Diffusion对500年西方绘画进行分析，从中提取两类隐含信息：形式特征（如色彩）与上下文特征（如主题）。结果表明，上下文信息在区分艺术时期、风格及艺术家方面优于形式特征。进一步通过从画作中提取的语境关键词，揭示艺术表达随社会变迁演进的规律。基于前瞻性语境的生成实验成功重现了艺术演化路径，凸显了社会与艺术之间的相互作用。该研究拓展了传统形式分析，将时间、文化与历史背景纳入多模态分析框架。
原文摘要 · Abstract (English)
The rise of multimodal generative AI is transforming the intersection of technology and art, offering deeper insights into large-scale artwork. Although its creative capabilities have been widely explored, its potential to represent artwork in latent spaces remains underexamined. We use cutting-edge generative AI, specifically Stable Diffusion, to analyze 500 years of Western paintings by extracting two types of latent information with the model: formal aspects (e.g., colors) and contextual aspects (e.g., subject). Our findings reveal that contextual information differentiates between artistic periods, styles, and individual artists more successfully than formal elements. Additionally, using contextual keywords extracted from paintings, we show how artistic expression evolves alongside societal changes. Our generative experiment, infusing prospective contexts into historical artworks, successfully reproduces the evolutionary trajectory of artworks, highlighting the significance of mutual interaction between society and art. This study demonstrates how multimodal AI expands traditional formal analysis by integrating temporal, cultural, and historical contexts.
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