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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: May 20, 2025

Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
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Interactive Face Video Coding: A Generative Compression Framework.

Bolin Chen, Zhao Wang, Binzhe Li

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    Summary
    This summary is machine-generated.

    This study introduces Interactive Face Video Coding (IFVC), enabling direct interaction with visual representations for ultra-compact, low-delay face video. The novel Internal Dimension Increase (IDI) method enhances rendering fidelity and supports vivid animation.

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    Area of Science:

    • Computer Vision
    • Digital Signal Processing
    • Multimedia Communications

    Background:

    • Traditional video coding methods struggle with interactive editing and semantic manipulation of face videos.
    • Existing generative compression techniques lack efficient interactive capabilities for real-time applications.

    Purpose of the Study:

    • To propose a novel framework for Interactive Face Video Coding (IFVC) that allows interaction with intrinsic visual representations.
    • To achieve ultra-compact representation, low-delay interaction, and vivid expression/headpose animation for face videos.

    Main Methods:

    • Development of an Internal Dimension Increase (IDI) based representation for enhanced fidelity and flexibility.
    • Projection of visual signals into controllable 3D semantics (e.g., mouth motion, head pose) for compression and transmission.
    • Utilizing deep generative models for synthesizing face frames from an editable, semantic-level bitstream.

    Main Results:

    • The proposed IFVC scheme outperforms state-of-the-art Versatile Video Coding (VVC) and generative compression methods in rate-distortion performance for face videos.
    • Demonstrated superior performance in enabling interactive coding without additional manipulation processes.
    • Achieved ultra-compact representation, low-delay interaction, and vivid animation capabilities.

    Conclusions:

    • The novel IFVC framework offers significant advantages over existing video coding standards for face videos.
    • The proposed IDI representation and semantic-level interactivity pave the way for advanced digital human communication.
    • This research has strong application prospects in the metaverse and future interactive media.