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Staking out curves is an essential process in construction to ensure the accurate alignment of structures along a curved path. This task involves positioning stakes at calculated locations corresponding to the curve's design, effectively translating plans into physical markers in the field. The process begins by determining the geometric parameters of the curve, including the radius, central angle, and tangent distances. These parameters are critical for identifying key points such as the...
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HairStyle Editing via Parametric Controllable Strokes.

Xinhui Song, Chen Liu, Youyi Zheng

    IEEE Transactions on Visualization and Computer Graphics
    |April 6, 2023
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    Summary
    This summary is machine-generated.

    This study introduces HairstyleNet, a novel network for interactive hairstyle editing. It simplifies hair manipulation by using parametric strokes, enabling users to easily change hairstyles in images.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Artificial Intelligence

    Background:

    • Existing methods for digital hairstyle editing are often complex and lack user-friendliness.
    • Interactive manipulation of hair regions in images remains a challenging task.

    Purpose of the Study:

    • To develop an intuitive and efficient stroke-based network for hairstyle editing.
    • To enable users to conveniently alter local or entire hairstyles in an image.

    Main Methods:

    • Introduced parametric strokes (quadratic Bézier curves with thickness) to represent hair wisps.
    • Utilized a neural renderer for differentiable mapping from stroke parameters to images.
    • Designed a two-stage network: stroke parameterization and stroke-to-hair generation.
    • Employed a hairstyle refinement network for high-fidelity hair generation from latent codes.

    Main Results:

    • HairstyleNet allows flexible and convenient manipulation of hairstyles.
    • The proposed method achieves state-of-the-art performance in hairstyle editing.
    • Demonstrated successful generation of high-fidelity images with edited hairstyles.

    Conclusions:

    • HairstyleNet offers a simplified and effective approach to interactive hairstyle editing.
    • The stroke-based parameterization and neural rendering enable differentiable hair manipulation.
    • The network facilitates user-driven changes to hairstyles with high visual quality.