Stochastic Interpolants for Revealing Stylistic Flows across the History of Art

Generative probabilistic models have rapidly advanced and are now widely used in content creation. They have achieved impressive results in generating artwork and demonstrated an understanding of different styles. However, their understanding of art primarily remains at the level of individual pieces, limiting their ability to reveal broader stylistic trends and transitions over time. To analyze how art evolves, a distributional perspective is required, as single-instance observations do not capture the relation between them, which is essential for such a study. In this work, we introduce a diverse and high-quality dataset of over $656{,}536$ artworks spanning various genres, including paintings, illustrations, and other art forms, along with relevant metadata and annotations.Building on this dataset, we present a method that models the evolution of art as an optimal transport problem with stochastic interpolant to examine stylistic changes over time without requiring paired data. This approach allows us to study and understand the historical progression of art, uncovering the transitions and stylistic shifts that have occurred over centuries. Our code and dataset will be released upon publication.