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Related Experiment Video

Updated: Nov 1, 2025

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing.

Meng Cao, Haozhi Huang, Hao Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 24, 2021
    PubMed
    Summary

    UniFaceGAN offers unified facial video editing for face swapping and reenactment. This novel framework ensures temporal consistency, producing more realistic and smoother video portraits than existing methods.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • 3D Graphics

    Background:

    • Facial image editing, including face swapping and reenactment, has advanced.
    • Current methods often address tasks individually and struggle with temporal consistency in videos, causing visual flickers.

    Purpose of the Study:

    • To introduce UniFaceGAN, a unified framework for temporally consistent facial video editing.
    • To enable simultaneous handling of face swapping and face reenactment.
    • To improve the realism and temporal smoothness of edited facial videos.

    Main Methods:

    • Utilized a 3D reconstruction model and a dynamic training sample selection mechanism.
    • Introduced a novel 3D temporal loss constraint based on barycentric coordinate interpolation for temporal consistency.
    • Proposed a region-aware conditional normalization layer for context-harmonious synthesis.

    Main Results:

    • UniFaceGAN successfully handles face swapping and face reenactment simultaneously.
    • The framework generates temporally consistent and photo-realistic video portraits.
    • Achieved superior results compared to state-of-the-art facial image editing methods.

    Conclusions:

    • UniFaceGAN provides a unified and temporally consistent approach to facial video editing.
    • The proposed methods enhance realism and reduce visual artifacts in edited videos.
    • This framework advances the capabilities of facial video manipulation.