Stochastic Interpolants for Revealing Stylistic Flows Across the History of Art

MCML Authors

Pingchuan Ma

→ Group Björn Ommer
 Computer Vision & Learning

Johannes Schusterbauer

→ Group Björn Ommer
 Computer Vision & Learning

Olga Grebenkova

→ Group Björn Ommer
 Computer Vision & Learning

Vincent Tao Hu

Dr.

→ Group Björn Ommer
 Computer Vision & Learning

Björn Ommer

Prof. Dr.

Principal Investigator

Computer Vision & Learning

Abstract

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.

inproceedings

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ICCV 2025

IEEE/CVF International Conference on Computer Vision. Honolulu, Hawai'i, Oct 19-23, 2025. To be published. Preprint available.

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Authors

P. Ma • M. Gui • J. Schusterbauer • X. Yang • O. Grebenkova • V. T. Hu • B. Ommer

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Links

URL GitHub

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Research Area

 B1 | Computer Vision