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3DFACENet: 3D Facial Attractiveness Computation and Enhancement Network.

Yuan Xie, Tianhao Peng, Mu Li

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    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.

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    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.