Stochastic Interpolants for Revealing Stylistic Flows across the History of Art

Pingchuan Ma, Ming Gui, Johannes Schusterbauer, Xiaopei Yang, Olga Grebenkova, Vincent Tao Hu, Björn Ommer; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 5867-5878

Generative models have made rapid progress in content creation, particularly in synthesizing artworks and capturing stylistic variation. However, most methods operate at the level of individual images, limiting their ability to reveal broader stylistic trends and temporal transitions. We address this by introducing a framework that models stylistic evolution as an optimal transport problem in a learned style space, using stochastic interpolants and dual diffusion implicit bridges to align artistic distributions across time without requiring paired data. A central contribution is a diverse dataset of over 650,000 artworks spanning 500 years, curated with metadata across multiple genres. Together, our method and dataset enable tracing long-range stylistic transitions and plausible futures of individual artworks, supporting fine-grained temporal analysis. This offers a new tool for modeling historical patterns in visual culture and opens up promising directions in visual understanding. Code and dataset: https://github.com/CompVis/Art-fm