Revisiting Wölfflin in the Age of AI: A Study of Classical and Baroque Composition in Generative Models
Year: 2025
Type: article
Abstract: This study explores how contemporary text-to-image models interpret and generate Classical and Baroque styles under Wölfflin's framework-two categories that are atemporal and transversal across media. Our goal is to see whether generative AI can replicate the nuanced stylistic cues that art historians attribute to them. We prompted two popular models (DALL•E and Midjourney) using explicit style labels (e.g., "baroque" and "classical") as well as more implicit cues (e.g., "dynamic", "static", or reworked Wölfflin descriptors). We then collected expert ratings and conducted broader qualitative reviews to assess how each output aligned with Wölfflin's characteristics. Our findings suggest that the term "baroque" usually evokes features recognizable in typically historical Baroque artworks, while "classical" often yields less distinct results, particularly when a specified genre (portrait, still life) imposes a centered, closed-form composition. Removing explicit style labels may produce highly abstract images, revealing that Wölfflin's descriptors alone may be insufficient to convey Classical or Baroque styles efficiently. Interestingly, the term "dynamic" gives rather chaotic images, yet this chaos is somehow ordered, centered, and has an almost Classical feel. Altogether, these observations highlight the complexity of bridging canonical stylistic frameworks and contemporary AI training biases, underscoring the need to update or refine Wölfflin's atemporal categories to accommodate how generative models-and modern visual culture-reinterpret Classical and Baroque.
Source: Journal of Imaging
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W4409658727