Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?
Visual Computing Center (VCC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-CRG2017-3426
Permanent link to this recordhttp://hdl.handle.net/10754/660312
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AbstractWe propose an efficient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHD dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.
CitationAbdal, R., Qin, Y., & Wonka, P. (2019). Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space? 2019 IEEE/CVF International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2019.00453
SponsorsThis work was supported by the KAUST Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3426.
Conference/Event name2019 IEEE/CVF International Conference on Computer Vision (ICCV)
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