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Muscles for Facial Expressions01:14

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The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
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Related Experiment Video

Updated: Jun 2, 2026

Reverse Dissection and DiceCT Reveal Otherwise Hidden Data in the Evolution of the Primate Face
08:15

Reverse Dissection and DiceCT Reveal Otherwise Hidden Data in the Evolution of the Primate Face

Published on: January 7, 2019

Reconstructing 3D Face Model with Associated Expression Deformation from a Single Face Image via Constructing a

Shu-Fan Wang, Shang-Hong Lai

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 18, 2011
    PubMed
    Summary

    This study introduces a novel 3D face reconstruction method using a manifold-based approach. It accurately estimates 3D face models and expression deformations from single images, advancing facial animation and recognition.

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    Published on: January 7, 2019

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    High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques

    Published on: December 3, 2013

    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Machine Learning

    Background:

    • Facial expression modeling is crucial for facial expression recognition and synthesis in animation.
    • Accurate 3D face reconstruction from single images remains a challenge.

    Purpose of the Study:

    • To propose a manifold-based 3D face reconstruction approach for estimating 3D face models and expression deformations.
    • To develop a novel algorithm for reconstructing 3D face geometry and facial deformation from a single image.

    Main Methods:

    • Utilized a robust weighted feature map (RWF) for dense correspondences between 3D face models.
    • Constructed a nonlinear 3D expression manifold from a large set of 3D facial expression models.
    • Learned a Gaussian mixture model on the manifold to represent expression deformation distribution.
    • Employed an energy minimization framework combining a morphable neutral face model and the expression manifold.

    Main Results:

    • Successfully reconstructed 3D face geometry and facial deformation from single images.
    • Demonstrated the effectiveness and accuracy of the proposed algorithm on simulated and real images.
    • Achieved accurate dense correspondences using the RWF.

    Conclusions:

    • The proposed manifold-based 3D face reconstruction approach is effective and accurate.
    • This method advances facial expression modeling for recognition and animation applications.
    • The integration of morphable models and low-dimensional manifolds offers a powerful framework for 3D face reconstruction.