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Localizing Protein in 3D Neural Stem Cell Culture: a Hybrid Visualization Methodology
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Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation.

Xuechao Zou, Shun Zhang, Xing Fu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
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    Summary

    Face-MoGLE enhances controllable face generation by decoupling semantic attributes and using specialized experts for realistic, fine-grained control. New datasets and experiments show superior performance and realism, even fooling deepfake detectors.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Generative Models

    Background:

    • Controllable face generation faces challenges in balancing semantic control and photorealism.
    • Existing methods struggle with disentangling attributes and capturing both global structure and local semantics.

    Purpose of the Study:

    • Introduce Face-MoGLE, a novel framework for advanced controllable face generation.
    • Improve fine-grained attribute control and photorealism in generated faces.

    Main Methods:

    • Semantic-decoupled latent modeling via mask-conditioned factorization.
    • Mixture of global and local experts for structural and semantic refinement.
    • Diffusion-aware dynamic gating network for evolving denoising processes.
    • Creation of multimodal datasets (MM-FFHQ-Female, MM-FairFace-HQ) for evaluation.

    Main Results:

    • Face-MoGLE outperforms state-of-the-art methods in unimodal and multimodal settings.
    • Achieved superior image quality, semantic alignment, and aesthetic preference.
    • Generated images are realistic enough to challenge deepfake detection models.
    • Demonstrated strong zero-shot generalization capabilities.

    Conclusions:

    • Face-MoGLE offers a powerful solution for controllable face generation.
    • The proposed methods and datasets advance the field of generative AI.
    • Highlights the importance of responsible AI development in generative modeling.