Abstract
We 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.
| Original language | English (US) |
|---|---|
| Title of host publication | 2019 IEEE/CVF International Conference on Computer Vision (ICCV) |
| Publisher | IEEE |
| Pages | 4431-4440 |
| Number of pages | 10 |
| ISBN (Print) | 9781728148038 |
| DOIs | |
| State | Published - 2019 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): OSR-CRG2017-3426
Acknowledgements: This work was supported by the KAUST Office of Sponsored Research (OSR) under Award No. OSR-CRG2017-3426.