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Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
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ArtCrafter: Text-Image Aligning Artistic Attribute Transfer via Embedding Reframing.

Nisha Huang, Kaer Huang, Yifan Pu

    IEEE Transactions on Visualization and Computer Graphics
    |December 23, 2025
    PubMed
    Summary

    ArtCrafter enables advanced artistic attribute transfer by using diffusion models to capture high-level aesthetics like composition and pose. This innovative framework offers enhanced control and diversity in text-to-image generation.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Diffusion models have significantly advanced text-guided style transfer.
    • Traditional style transfer focuses on low-level features, unlike artistic attribute transfer.

    Purpose of the Study:

    • Propose ArtCrafter, an innovative text-to-image artistic attribute transfer framework.
    • Enable transfer of high-level aesthetic attributes beyond simple visual features.

    Main Methods:

    • Developed an attention-based style extraction module with a multi-layer architecture and perceiver attention.
    • Introduced a text-image aligning augmentation component for shared feature space mapping.
    • Incorporated explicit modulation with embedding reframing for diverse output generation.

    Main Results:

    • ArtCrafter demonstrates impressive visual stylization results.
    • Achieved exceptional artistic attribute intensity, controllability, and diversity.
    • Experimental validation confirms the framework's effectiveness.

    Conclusions:

    • ArtCrafter successfully transfers high-level artistic attributes from text and images.
    • The framework offers superior control and diversity compared to traditional methods.
    • This work advances the field of text-guided image generation and artistic style transfer.