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    This summary is machine-generated.

    This study introduces a novel framework for cross-domain face translation, enhancing image quality by integrating multi-view knowledge and frequency consistency. The method effectively addresses structural deformation and identity ambiguity in generated faces.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Artificial Intelligence

    Background:

    • Cross-domain face translation is crucial for applications like law enforcement and security.
    • Existing methods struggle with limited data, causing structural deformation and identity ambiguity.

    Purpose of the Study:

    • To develop a robust framework for cross-domain face translation that overcomes limitations of existing methods.
    • To improve the perceptual appearance and accuracy of translated face images.

    Main Methods:

    • Proposed a multi-view knowledge (structural and identity) ensemble framework with frequency consistency (MvKE-FC).
    • Introduced an attention-based knowledge aggregation module for effective knowledge fusion.
    • Developed a frequency-consistent (FC) loss incorporating multidirection Prewitt (mPrewitt) and Gaussian blur losses.

    Main Results:

    • The MvKE-FC framework significantly improves generative performance on limited cross-domain face image pairs.
    • Experimental results demonstrate superior qualitative and quantitative performance compared to state-of-the-art methods.
    • The proposed FC loss enhances generative models, showing broad applicability.

    Conclusions:

    • The MvKE-FC framework effectively addresses challenges in cross-domain face translation.
    • Multi-view knowledge transfer and frequency consistency are key to improving face translation quality.
    • The developed FC loss offers a flexible enhancement for various generative models.