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Compositional Model-Based Sketch Generator in Facial Entertainment.

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    This study introduces a novel compositional model for face sketch synthesis (FSS) that overcomes over-smoothing issues. The method enhances detail preservation and improves overall synthesis performance in facial entertainment applications.

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    Area of Science:

    • Computer Vision
    • Image Synthesis
    • Artificial Intelligence

    Background:

    • Existing face sketch synthesis (FSS) methods often produce over-smoothed results, suppressing fine facial details.
    • This limitation impacts the quality and realism of generated sketches for entertainment purposes.

    Purpose of the Study:

    • To propose a novel compositional face sketch generator that addresses the over-smoothing problem in FSS.
    • To improve the synthesis performance and detail preservation in face sketch generation.

    Main Methods:

    • A compositional model that decomposes faces into components with candidate templates.
    • Utilizing multilevel B-spline approximation for template polishing.
    • Employing Poisson blending for component fusion.

    Main Results:

    • The proposed compositional method significantly reduces high-frequency loss compared to existing FSS techniques.
    • Demonstrated improved synthesis performance across various FSS tasks, including morphing, expression manipulation, and multiview synthesis.

    Conclusions:

    • The compositional approach effectively preserves fine details, overcoming the over-smoothing limitations of prior methods.
    • The method shows strong effectiveness in diverse face sketch synthesis applications, enhancing facial entertainment.