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Photorealistic Audio-driven Video Portraits.

Xin Wen, Miao Wang, Christian Richardt

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    |September 17, 2020
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    This study introduces a new method for creating realistic video portraits driven by voice. The approach synthesizes photorealistic facial expressions from speech audio, outperforming existing methods in user studies.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Computer Graphics

    Background:

    • Video portraits are widely used in applications like videoconferencing and virtual training.
    • Synthesizing photorealistic facial expressions from speech audio presents a significant challenge.

    Purpose of the Study:

    • To develop a novel method for synthesizing photorealistic video portraits driven by voice.
    • To address the challenge of hallucinating plausible facial expressions from speech audio.

    Main Methods:

    • Utilized a parametric 3D face model to learn audio features to model parameter mapping.
    • Predicted facial expression parameters from high-dimensional audio features.
    • Employed a neural face renderer to generate photorealistic video portraits.

    Main Results:

    • The proposed method successfully synthesizes photorealistic video portraits driven by voice.
    • Demonstrated generalization capabilities with various input speech audio, including text-to-speech.
    • Outperformed previous general-purpose audio-driven video portrait methods in experiments and user studies.

    Conclusions:

    • The novel method effectively synthesizes realistic audio-driven video portraits.
    • The approach offers a significant advancement in synthesizing facial expressions from speech.
    • Results indicate superior realism compared to existing methods.