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arXiv · 2601.17697

StyleDecoupler: Generalizable Artistic Style Disentanglement

Zexi Jia·Jinchao Zhang·Jie Zhou
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Abstract

Representing artistic style is challenging due to its deep entanglement with semantic content. We propose StyleDecoupler, an information-theoretic framework that leverages a key insight: multi-modal vision models encode both style and content, while uni-modal models suppress style to focus on content-invariant features. By using uni-modal representations as content-only references, we isolate pure style features from multi-modal embeddings through mutual information minimization. StyleDecoupler operates as a plug-and-play module on frozen Vision-Language Models without fine-tuning. We also introduce WeART, a large-scale benchmark of 280K artworks across 152 styles and 1,556 artists. Experiments show state-of-the-art performance on style retrieval across WeART and WikiART, while enabling applications like style relationship mapping and generative model evaluation. We release our method and dataset at this url.

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Zexi Jia, Jinchao Zhang, Jie Zhou. 2026-01-25. StyleDecoupler: Generalizable Artistic Style Disentanglement. https://arxiv.org/abs/2601.17697

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