Deliege Adrien, Dondero Maria Giulia, D'Armenio Enzo
Department of Romance Languages and Literatures, Faculty of Philosophy and Letters, University of Liège, 4000 Liège, Belgium.
Department of Electrical Engineering and Computer Science, Montefiore Institute, Faculty of Applied Sciences, University of Liège, 4000 Liège, Belgium.
J Imaging. 2025 Apr 22;11(5):128. doi: 10.3390/jimaging11050128.
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.
本研究探讨了当代文本到图像模型如何在沃尔夫林的框架下解读和生成古典风格与巴洛克风格，这两类风格不受时间限制且跨越不同媒介。我们的目标是看看生成式人工智能能否复制艺术史学家归因于它们的细微风格线索。我们使用明确的风格标签（如“巴洛克”和“古典”）以及更隐晦的线索（如“动态”“静态”或改编后的沃尔夫林描述词）来提示两个流行的模型（DALL•E和Midjourney）。然后我们收集了专家评分并进行了更广泛的定性评估，以评估每个输出与沃尔夫林的特征的契合程度。我们的研究结果表明，“巴洛克”一词通常会唤起典型历史巴洛克艺术作品中可识别的特征，而“古典”通常会产生不太明显的结果，尤其是当指定的体裁（肖像、静物）采用中心对称、封闭形式的构图时。去除明确的风格标签可能会产生高度抽象的图像，这表明仅靠沃尔夫林的描述词可能不足以有效地传达古典或巴洛克风格。有趣的是，“动态”一词会产生相当混乱的图像，但这种混乱在某种程度上是有序的、以中心为导向的，并且几乎有一种古典的感觉。总之，这些观察结果凸显了弥合经典风格框架与当代人工智能训练偏差的复杂性，强调需要更新或完善沃尔夫林的无时间限制的类别，以适应生成模型以及现代视觉文化对古典和巴洛克风格的重新诠释。
