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Related Experiment Video

Updated: May 29, 2026

Single-stage Dynamic Reanimation of the Smile in Irreversible Facial Paralysis by Free Functional Muscle Transfer
19:53

Single-stage Dynamic Reanimation of the Smile in Irreversible Facial Paralysis by Free Functional Muscle Transfer

Published on: March 1, 2015

Facial Performance Transfer via Deformable Models and Parametric Correspondence.

Akshay Asthana, Miles de la Hunty, Abhinav Dhall

    IEEE Transactions on Visualization and Computer Graphics
    |September 21, 2011
    PubMed
    Summary

    This study introduces a new method for realistic facial performance transfer using Active Appearance Models (AAMs). It enables seamless expression and texture mapping across different faces, even for cross-language dubbing.

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    Creating Virtual-hand and Virtual-face Illusions to Investigate Self-representation
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    Published on: March 1, 2015

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    06:53

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    Published on: March 1, 2017

    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Facial performance transfer is crucial for the film and computer graphics industries.
    • Deformable face models, like Active Appearance Models (AAMs), enable real-time face tracking and synthesis.
    • Existing methods for facial performance transfer using deformable models are of significant interest.

    Purpose of the Study:

    • To propose a novel approach for real-time facial performance transfer within the Active Appearance Model (AAM) framework.
    • To learn a mapping between parameters of independent AAMs for more realistic facial performance transfer.
    • To enable meaningful transfer of nonrigid shape and texture across diverse faces and conditions.

    Main Methods:

    • Utilizing the Active Appearance Model (AAM) framework for facial performance transfer.
    • Learning parametric correspondence between two independent AAMs.
    • Exploring linear and nonlinear methods, specifically sparse linear regression, for modeling parametric correspondence.

    Main Results:

    • The proposed method facilitates more realistic facial performance transfer compared to previous approaches.
    • Parametric correspondence modeling allows for the transfer of nonrigid shape and texture.
    • The framework is effective irrespective of speaker gender, face shape/size, and illumination.
    • Sparse linear regression proved to be the most effective method for modeling parametric correspondence.

    Conclusions:

    • The developed framework enables realistic, parameter-driven facial performance transfer across diverse individuals.
    • The approach successfully handles variations in gender, facial structure, and lighting conditions.
    • The method demonstrates utility for cross-language facial performance transfer, benefiting the movie dubbing industry.