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FM-3DFR: Facial Manipulation-Based 3-D Face Reconstruction.
IEEE Transactions on Cybernetics
|April 7, 2023
Summary
This study introduces a novel framework for 3-D face reconstruction, enhancing 3-D Morphable Models (3DMM) with personalized shapes and improved expression representation for better facial fitting in images.
Area of Science:
- Computer Vision
- 3D Computer Graphics
- Machine Learning
Background:
- 3-D Morphable Models (3DMM) are crucial for 3-D face analysis.
- Existing 3-D face reconstruction methods struggle with expression representation due to data imbalance and lack of ground-truth 3-D shapes.
Purpose of the Study:
- To develop a novel framework for learning personalized 3-D face shapes.
- To enhance the accuracy and robustness of 3-D face reconstruction, particularly in representing facial expressions and poses.
Main Methods:
- Dataset augmentation to balance facial shape and expression distribution.
- Mesh editing for expression synthesis and generating diverse facial expressions.
- Improved pose estimation by transferring projection parameters to Euler angles.
- Weighted sampling based on vertex offset for robust training.
Main Results:
- The proposed method achieves state-of-the-art performance on challenging benchmarks.
- Demonstrated superior accuracy in reconstructing personalized 3-D face shapes.
- Enhanced capability in representing a wide range of facial expressions.
Conclusions:
- The novel framework significantly improves 3-D face reconstruction by addressing limitations in personalized shape and expression modeling.
- The method offers a robust and accurate solution for 3-D face analysis tasks.
