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Data-Driven Sketch Beautification With Neural Feature Representation.

I-Chao Shen

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    Summary
    This summary is machine-generated.

    This study introduces a data-driven method to enhance freehand sketches by leveraging artist-drawn vector shapes. The approach uses neural networks to improve local and global visual properties, resulting in more appealing sketch beautification across various object categories.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Artificial Intelligence

    Background:

    • Freehand sketches often lack the visual appeal of artist-drawn vector shapes.
    • Existing methods may not effectively capture local and global visual merits for sketch beautification.
    • Object categories present unique challenges for sketch enhancement.

    Purpose of the Study:

    • To develop a data-driven approach for beautifying freehand sketches.
    • To utilize artist-drawn vector shapes for improving sketch aesthetics.
    • To create a method that enhances both local and global visual properties of sketches.

    Main Methods:

    • A neural network is employed to model local and global merits across diverse object categories.
    • Sample points are matched between input sketches and vector shapes using extracted feature representations.
    • An optimization problem is formulated to ensure sketch resemblance to vector shapes while preserving original style and semantics.

    Main Results:

    • The proposed method successfully beautifies freehand sketches.
    • The approach enhances visual appeal by improving local and global properties.
    • Demonstrated effectiveness across various sketch categories.

    Conclusions:

    • The data-driven approach effectively beautifies freehand sketches using vector shape guidance.
    • Neural networks can represent and leverage visual merits for sketch enhancement.
    • The method offers a promising technique for improving the aesthetic quality of digital sketches.