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Shape-Matching GAN++: Scale Controllable Dynamic Artistic Text Style Transfer.

Shuai Yang, Zhangyang Wang, Jiaying Liu

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 28, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces controllable artistic text animation by enabling users to adjust the degree of shape deformation in dynamic text style transfer. The novel bidirectional shape matching framework achieves high-quality and consistent results.

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    Area of Science:

    • Computer Vision
    • Computer Graphics
    • Artificial Intelligence

    Background:

    • Dynamic artistic text style transfer synthesizes text animations by migrating appearance and motion from reference videos.
    • Existing methods offer texture control but lack precise control over shape deformation.
    • Controlling the stylistic degree of shape deformation remains an open challenge in text animation.

    Purpose of the Study:

    • To introduce and address the problem of dynamic artistic text style transfer with glyph stylistic degree control.
    • To develop a novel framework for learning multi-scale glyph-style shape mappings.
    • To enable controllable artistic text animation with adjustable shape deformation.

    Main Methods:

    • A scale-aware Shape-Matching Generative Adversarial Network (GAN) learns multi-scale shape mappings for style transfer.
    • An advanced Shape-Matching GAN++ is proposed for animating static text images using reference style videos.
    • The framework utilizes bidirectional shape matching and ensures short-term and long-term motion consistency.

    Main Results:

    • The proposed method successfully transfers style, including appearance and motion, to target text.
    • Glyph stylistic degree control is achieved, allowing adjustable shape deformation.
    • Experiments demonstrate superior qualitative and quantitative performance compared to state-of-the-art methods.

    Conclusions:

    • The developed Shape-Matching GAN++ framework effectively generates high-quality, controllable artistic text animations.
    • The method addresses the challenge of shape deformation control in dynamic text style transfer.
    • This work advances the field by providing precise control over stylistic elements in text animation.