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A Domain-Guided Noise-Optimization-Based Inversion Method for Facial Image Manipulation.

Nan Yang, Zeyu Zheng, Mengchu Zhou

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
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    Summary
    This summary is machine-generated.

    This study introduces Domain-guided Noise-optimization-based Inversion (DNI) for facial image manipulation using StyleGAN2. DNI enhances image reconstruction quality and semantic accuracy, outperforming existing methods.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Style-based generative adversarial networks (StyleGAN2) excel in unconditional image generation.
    • Facial image manipulation requires high-fidelity reconstruction and semantic preservation.

    Purpose of the Study:

    • To propose a novel method, Domain-guided Noise-optimization-based Inversion (DNI), for facial image manipulation.
    • To improve the quality and semantic accuracy of image reconstruction in generative models.

    Main Methods:

    • Developed Image2latent, a domain-guided encoder for projecting images into the StyleGAN2 latent space.
    • Incorporated a noise optimization mechanism to capture high-frequency details and enhance reconstruction.
    • Utilized a mask for seamless image fusion and local style migration.
    • Proposed a semantic alignment evaluation pipeline using attribute boundaries.

    Main Results:

    • DNI achieves high-quality image reconstruction while preserving semantic meaning.
    • The noise optimization mechanism effectively captures high-frequency details, improving edge representation.
    • The method demonstrates successful facial image manipulation and outperforms state-of-the-art techniques.
    • Extensive evaluations confirm DNI's ability to capture rich semantic information.

    Conclusions:

    • DNI offers a robust solution for facial image manipulation with superior reconstruction and semantic alignment.
    • The proposed evaluation pipeline provides a reliable measure of semantic alignment for inverse codes.
    • DNI advances the capabilities of generative models for complex image editing tasks.