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    • 38-Issue 7
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    Two-phase Hair Image Synthesis by Self-Enhancing Generative Model

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    Date
    2019
    Author
    Qiu, Haonan ORCID
    Wang, Chuan ORCID
    Zhu, Hang ORCID
    zhu, xiangyu ORCID
    Gu, Jinjin ORCID
    Han, Xiaoguang ORCID
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    Abstract
    Generating plausible hair image given limited guidance, such as sparse sketches or low-resolution image, has been made possible with the rise of Generative Adversarial Networks (GANs). Traditional image-to-image translation networks can generate recognizable results, but finer textures are usually lost and blur artifacts commonly exist. In this paper, we propose a two-phase generative model for high-quality hair image synthesis. The two-phase pipeline first generates a coarse image by an existing image translation model, then applies a re-generating network with self-enhancing capability to the coarse image. The selfenhancing capability is achieved by a proposed differentiable layer, which extracts the structural texture and orientation maps from a hair image. Extensive experiments on two tasks, Sketch2Hair and Hair Super-Resolution, demonstrate that our approach is able to synthesize plausible hair image with finer details, and reaches the state-of-the-art.
    BibTeX
    @article {10.1111:cgf.13847,
    journal = {Computer Graphics Forum},
    title = {{Two-phase Hair Image Synthesis by Self-Enhancing Generative Model}},
    author = {Qiu, Haonan and Wang, Chuan and Zhu, Hang and zhu, xiangyu and Gu, Jinjin and Han, Xiaoguang},
    year = {2019},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.13847}
    }
    URI
    https://doi.org/10.1111/cgf.13847
    https://diglib.eg.org:443/handle/10.1111/cgf13847
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