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    Improved StyleGAN Embedding: Where are the Good Latents?

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    Type
    Preprint
    Authors
    Zhu, Peihao
    Abdal, Rameen
    Qin, Yipeng
    Wonka, Peter cc
    KAUST Department
    Computer Science Program
    Computer Science
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    KAUST.
    Visual Computing Center (VCC)
    Date
    2020-12-13
    Permanent link to this record
    http://hdl.handle.net/10754/666570
    
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    Abstract
    StyleGAN is able to produce photorealistic images almost indistinguishable from real ones. Embedding images into the StyleGAN latent space is not a trivial task due to the reconstruction quality and editing quality trade-off. In this paper, we first introduce a new normalized space to analyze the diversity and the quality of the reconstructed latent codes. This space can help answer the question of where good latent codes are located in latent space. Second, we propose a framework to analyze the quality of different embedding algorithms. Third, we propose an improved embedding algorithm based on our analysis. We compare our results with the current state-of-the-art methods and achieve a better trade-off between reconstruction quality and editing quality.
    Publisher
    arXiv
    arXiv
    2012.09036
    Additional Links
    https://arxiv.org/pdf/2012.09036
    Collections
    Preprints; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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