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Updated: May 31, 2026

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SD-ReID: View-Aware Stable Diffusion for Aerial-Ground Person Re-Identification.

Yuhao Wang, Xiang Hu, Lixin Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 28, 2026
    PubMed
    Summary
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    This study introduces SD-ReID, a generative framework for Aerial-Ground Person Re-Identification (AG-ReID). It enhances person recognition across diverse camera views by leveraging Stable Diffusion for robust identity representation.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Aerial-Ground Person Re-Identification (AG-ReID) is challenging due to drastic viewpoint changes.
    • Existing methods struggle with view robustness and overlook view-specific features.

    Purpose of the Study:

    • To propose a novel generative framework, SD-ReID, for enhancing AG-ReID.
    • To improve identity representation by mimicking feature distributions across different views.
    • To extract robust identity representations while utilizing view-specific features.

    Main Methods:

    • A ViT-based model extracts person representations with identity and view conditions.
    • Stable Diffusion (SD) is fine-tuned to enhance representations guided by these conditions.
    • A View-Refined Decoder (VRD) bridges instance-level and global-level features.

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    Main Results:

    • The SD-ReID framework demonstrates effectiveness on five AG-ReID benchmarks.
    • The method successfully enhances person recognition across diverse camera viewpoints.
    • Generative models are leveraged to improve feature distribution mimicry and identity consistency.

    Conclusions:

    • SD-ReID offers a novel generative approach for AG-ReID.
    • The framework effectively addresses viewpoint variations in person re-identification.
    • The proposed method achieves state-of-the-art performance on multiple AG-ReID datasets.