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The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.

Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?

Open access
Abdal, Rameen, Qin, Yipeng, Wonka, Peter·2019·2019 IEEE/CVF International Conference on Computer Vision (ICCV)

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.

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Metadata

Authors
Abdal, R., Qin, Y., & Wonka, P.
Venue
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher
IEEE
Published
2019-10
Pages
4431-4440
OpenAlex
W2934375473
Cited by
1,076
References
31
FWCI
41.32
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space? https://app.cochat.ai/papers/10.1109/iccv.2019.00453
The paper
Abdal, 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). https://doi.org/10.1109/iccv.2019.00453

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.