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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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Updated: Jan 14, 2026

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CoFaCo: Controllable Generative Talking Face Video Coding.

Xiaona Li, Xihua Sheng, Meng Wang

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |January 12, 2026
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    Summary
    This summary is machine-generated.

    This study introduces Controllable Generative Talking Face Video Coding (CoFaCo), a new framework for efficient talking face video compression and control. CoFaCo integrates facial expression and head pose customization directly into the video coding process for improved performance.

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    Area of Science:

    • Computer Vision
    • Video Compression
    • Machine Learning

    Background:

    • Efficient talking face video coding and control are vital for modern communication.
    • Current methods applying control before coding lack satisfactory compression efficiency.

    Purpose of the Study:

    • To propose an efficient framework, Controllable Generative Talking Face Video Coding (CoFaCo), integrating control into the video coding process.
    • To enable user-customizable facial expressions and head poses with improved compression.

    Main Methods:

    • CoFaCo projects talking face videos into compact, semantic feature representations.
    • Sophisticated losses decouple pose and expression direction codes for independent control.
    • Generators create controlled pose and expression, with smoothing for temporal consistency.

    Main Results:

    • CoFaCo demonstrates competitive compression efficiency at ultra-low bit rates.
    • The framework achieves effective video reconstruction and control tasks.
    • Decoupled pose and expression codes enhance control accuracy and temporal stability.

    Conclusions:

    • CoFaCo offers an efficient solution for controllable talking face video coding.
    • The integrated approach advances face video communication with enhanced control capabilities.
    • This framework provides valuable insights for future developments in personalized video communication.