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Yuval Alaluf
Researcher at Tel Aviv University
Publications - 18
Citations - 1633
Yuval Alaluf is an academic researcher from Tel Aviv University. The author has contributed to research in topics: Computer science & Encoder. The author has an hindex of 5, co-authored 10 publications receiving 163 citations.
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Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
Elad Richardson,Yuval Alaluf,Or Patashnik,Yotam Nitzan,Yaniv Azar,Stav Shapiro,Daniel Cohen-Or +6 more
TL;DR: This work presents a generic image-to-image translation framework, pixel2style2pixel (pSp), 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 latent space.
Proceedings ArticleDOI
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
Elad Richardson,Yuval Alaluf,Or Patashnik,Yotam Nitzan,Yaniv Azar,Stav Shapiro,Daniel Cohen-Or +6 more
TL;DR: The pixel2style2pixel (pSp) as discussed by the authors 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.
Proceedings ArticleDOI
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
TL;DR: This work uses only 3 - 5 images of a user-provided concept to represent it through new “words” in the embedding space of a frozen text-to-image model, which can be composed into natural language sentences, guiding personalized creation in an intuitive way.
Journal ArticleDOI
Designing an encoder for StyleGAN image manipulation
TL;DR: In this article, the authors identify and analyze the existence of a distortioneditability tradeoff and a distortionperception tradeoff within the StyleGAN latent space, and suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on.
Journal ArticleDOI
Only a matter of style: age transformation using a style-based regression model
TL;DR: In this article, an image-to-image translation method that learns to directly encode real facial images into the latent space of a pre-trained unconditional GAN (e.g., StyleGAN) subject to a given aging shift is presented.