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    Area of Science:

    • Computer Graphics
    • Artificial Intelligence

    Background:

    • 3D face shape stylization transforms realistic faces into stylized versions (e.g., cartoons).
    • Traditional deformation transfer methods are slow and require re-optimization for each new face.

    Purpose of the Study:

    • To develop a faster and more efficient method for 3D face shape stylization.
    • To address the limitations of traditional deformation transfer techniques.

    Main Methods:

    • Proposed a neural network-based approach for 3D face shape stylization.
    • Employed weakly supervised learning and a novel template-guided mesh smoothing regularization.
    • Modeled the task as a deformation transfer problem.

    Main Results:

    • Achieved stylization quality comparable to traditional methods (average Chamfer Distance ~0.01 mm).
    • Significantly improved processing speed, approximately 3,000 times faster than traditional methods.
    • Eliminated the need for paired training data.

    Conclusions:

    • The proposed neural network method offers an efficient and practical solution for 3D face shape stylization.
    • This learning-based deformation transfer method is the first of its kind for 3D face stylization.
    • The method preserves template structure while enabling rapid stylization.