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Related Experiment Video

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Emotion-Preserving Blendshape Update With Real-Time Face Tracking.

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    IEEE Transactions on Visualization and Computer Graphics
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    Summary
    This summary is machine-generated.

    This study introduces a real-time facial animation technique that updates blendshapes using emotion information, preserving semantics and user-specific details without predefined expressions.

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    Area of Science:

    • Computer Graphics
    • Human-Computer Interaction
    • Computer Vision

    Background:

    • Blendshape representations are crucial for facial animation but struggle with identity-specific variations.
    • Existing methods require users to perform predefined expressions, limiting naturalness.

    Purpose of the Study:

    • To develop a real-time technique for updating facial blendshapes directly from arbitrary motion sequences.
    • To preserve semantic consistency and user-specific facial features, including wrinkles, during animation.

    Main Methods:

    • Proposing a novel approach that leverages facial emotion information to define blendshape semantics.
    • Developing algorithms for efficient, real-time updates of large-, middle-, and fine-scale facial geometry.
    • Utilizing commodity RGBD sensors for data acquisition and real-time face tracking.

    Main Results:

    • Achieved real-time online blendshape updates with well-preserved semantic consistency.
    • Successfully captured and updated user-specific facial features and fine details like wrinkles.
    • Demonstrated effectiveness using commodity RGBD sensors.

    Conclusions:

    • Facial emotion information can effectively define blendshape semantics for arbitrary facial motions.
    • The proposed technique enables real-time, semantically consistent facial animation with personalized details.
    • This method offers a more natural and efficient approach to facial blendshape animation.