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Cited by 1,072

The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.

Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

Open access
Richardson, Elad, Alaluf, Yuval, Patashnik, Or, Nitzan, Yotam, Azar, Yaniv, Shapiro, Stav, Cohen-Or, Daniel·2021·2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Abstract

We present a generic image-to-image translation framework, pixel2style2pixel (pSp). Our pSp framework is based on a novel encoder network that directly generates a series of style vectors which are fed into a pretrained StyleGAN generator, forming the extended $\mathcal{W} + $ latent space. We first show that our encoder can directly embed real images into $\mathcal{W} + $, with no additional optimization. Next, we propose utilizing our encoder to directly solve image-to-image translation tasks, defining them as encoding problems from some input domain into the latent domain. By deviating from the standard "invert first, edit later" methodology used with previous StyleGAN encoders, our approach can handle a variety of tasks even when the input image is not represented in the StyleGAN domain. We show that solving translation tasks through StyleGAN significantly simplifies the training process, as no adversary is required, has better support for solving tasks without pixel-to-pixel correspondence, and inherently supports multi-modal synthesis via the resampling of styles. Finally, we demonstrate the potential of our framework on a variety of facial image-to-image translation tasks, even when compared to state-of-the-art solutions designed specifically for a single task, and further show that it can be extended beyond the human facial domain. Code is available at https://github.com/eladrich/pixel2style2pixel.

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Metadata

Authors
Richardson, E., Alaluf, Y., Patashnik, O., Nitzan, Y., Azar, Y., Shapiro, S., & Cohen-Or, D.
Venue
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
IEEE
Published
2021-06
Pages
2287-2296
ISSN
1063-6919 (ISSN-L)
OpenAlex
W3046411017
Cited by
1,072
References
50
FWCI
74.33
Type
conference paper
Language
en
Open access
green

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CoChat. (2026). Citation check: Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation. https://app.cochat.ai/papers/10.1109/cvpr46437.2021.00232
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Richardson, E., Alaluf, Y., Patashnik, O., Nitzan, Y., Azar, Y., Shapiro, S., & Cohen-Or, D. (2021). Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr46437.2021.00232

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