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

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.