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High-resolution, High-speed, Three-dimensional Video Imaging with Digital Fringe Projection Techniques
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3DFACENet: 3D Facial Attractiveness Computation and Enhancement Network
Summary
This study introduces 3DFACENet for 3D facial attractiveness analysis and enhancement. It efficiently computes beauty scores using 3D shape and texture, significantly improving facial aesthetics while preserving identity.
Area of Science:
- Computer Vision
- Computer Graphics
- Human-Computer Interaction
Background:
- Advanced 3D facial attractiveness research is crucial for applications like virtual makeup and AR/VR.
- Existing research is limited by a lack of 3D beauty face data and complex data handling.
Purpose of the Study:
- To propose 3DFACENet, an innovative system for computing and enhancing 3D facial attractiveness.
- To address the computational complexity and data scarcity in 3D facial aesthetics research.
Main Methods:
- Utilized a 3D facial reconstruction encoder and a render module to generate 3D face models.
- Introduced an attractiveness computation module using 3D shape and texture coefficients for efficiency.
- Developed a controllable beautification decoder balancing aesthetic enhancement and identity preservation.
Main Results:
- Achieved state-of-the-art results in facial attractiveness computation by leveraging 3D coefficients.
- Introduced the concept of 'attractive centers' and demonstrated its correlation with beauty scores.
- Showcased significant and controllable enhancement in 3D facial attractiveness through coefficient editing.
Conclusions:
- 3DFACENet effectively computes and enhances 3D facial attractiveness.
- The proposed method offers a computationally efficient and feasible approach validated on benchmark datasets.
- The findings open new avenues for research in 3D facial aesthetics and its applications.
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