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    SEAN: Image Synthesis With Semantic Region-Adaptive Normalization

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    CVPR2020_SEAN.pdf
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    6.843Mb
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    Description:
    Accepted manuscript
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    supplemental_material.pdf
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    Type
    Conference Paper
    Authors
    Zhu, Peihao
    Abdal, Rameen
    Qin, Yipeng
    Wonka, Peter cc
    KAUST Department
    Computer Science
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Visual Computing Center (VCC)
    Date
    2020-08-05
    Online Publication Date
    2020-08-05
    Print Publication Date
    2020-06
    Permanent link to this record
    http://hdl.handle.net/10754/664682
    
    Metadata
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    Abstract
    We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image. Using SEAN normalization, we can build a network architecture that can control the style of each semantic region individually, e.g., we can specify one style reference image per region. SEAN is better suited to encode, transfer, and synthesize style than the best previous method in terms of reconstruction quality, variability, and visual quality. We evaluate SEAN on multiple datasets and report better quantitative metrics (e.g. FID, PSNR) than the current state of the art. SEAN also pushes the frontier of interactive image editing. We can interactively edit images by changing segmentation masks or the style for any given region. We can also interpolate styles from two reference images per region.
    Citation
    Zhu, P., Abdal, R., Qin, Y., & Wonka, P. (2020). SEAN: Image Synthesis With Semantic Region-Adaptive Normalization. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr42600.2020.00515
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    ISBN
    978-1-7281-7169-2
    DOI
    10.1109/CVPR42600.2020.00515
    arXiv
    1911.12861
    Additional Links
    https://ieeexplore.ieee.org/document/9156510/
    https://ieeexplore.ieee.org/document/9156510/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9156510
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPR42600.2020.00515
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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