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Image Embedding into Generative Adversarial Networks

We propose an e cient 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 FFHQ 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.

Identiferoai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/662516
Date14 April 2020
CreatorsAbdal, Rameen
ContributorsWonka, Peter, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Hadwiger, Markus, Ghanem, Bernard
Source SetsKing Abdullah University of Science and Technology
LanguageEnglish
Detected LanguageEnglish
TypeThesis

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