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StyleShot: A Snapshot on Any Style.

Junyao Gao, Yanan Sun, Yanchen Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 16, 2025
    PubMed
    Summary

    StyleShot introduces a style-aware encoder and StyleGallery dataset for effective image style transfer. This method efficiently mimics diverse styles without requiring computationally intensive test-time tuning.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Image style transfer aims to replicate a reference image's style using content from text or another image.
    • Current methods like fine-tuning embeddings or using CLIP image encoders face challenges with computational cost and suitability for style representation.

    Purpose of the Study:

    • To develop a generalized style transfer approach that overcomes the limitations of existing methods.
    • To create a style-aware encoder and a dedicated dataset (StyleGallery) for effective style representation learning.

    Main Methods:

    • Introduced a novel style-aware encoder designed for learning expressive style representations from multi-level image patches.
    • Developed StyleGallery, a well-organized style dataset to enhance generalization ability in style transfer.
    • Employed a content extraction and content-fusion encoder to improve image-driven style transfer.

    Main Results:

    • The proposed approach, StyleShot, effectively mimics various styles (3D, flat, abstract, fine-grained) without requiring test-time tuning.
    • StyleShot demonstrates superior performance compared to state-of-the-art text- and image-driven style transfer methods.
    • Achieved generalization across a wide range of styles due to the dedicated style learning design and dataset.

    Conclusions:

    • StyleShot offers a simple yet effective solution for generalized image style transfer.
    • The style-aware encoder and StyleGallery dataset are crucial for learning robust style representations.
    • The method eliminates the need for computationally expensive test-time tuning, making style transfer more accessible.