no code implementations • 18 Apr 2024 • Yotam Nitzan, Zongze Wu, Richard Zhang, Eli Shechtman, Daniel Cohen-Or, Taesung Park, Michaël Gharbi
We demonstrate that our approach is competitive with state-of-the-art inpainting methods in terms of quality and fidelity while providing a 10x speedup for typical user interactions, where the editing mask represents 10% of the image.
no code implementations • 12 Jul 2023 • Ariel Elazary, Yotam Nitzan, Daniel Cohen-Or
In this paper, we propose a novel method for facial reenactment using a personalized generator.
1 code implementation • CVPR 2023 • Yotam Nitzan, Michaël Gharbi, Richard Zhang, Taesung Park, Jun-Yan Zhu, Daniel Cohen-Or, Eli Shechtman
First, we note the generator contains a meaningful, pretrained latent space.
no code implementations • 31 Mar 2022 • Yotam Nitzan, Kfir Aberman, Qiurui He, Orly Liba, Michal Yarom, Yossi Gandelsman, Inbar Mosseri, Yael Pritch, Daniel Cohen-Or
Given a small reference set of portrait images of a person (~100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space.
no code implementations • 28 Feb 2022 • Amit H. Bermano, Rinon Gal, Yuval Alaluf, Ron Mokady, Yotam Nitzan, Omer Tov, Or Patashnik, Daniel Cohen-Or
Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks.
1 code implementation • ICLR 2022 • Zongze Wu, Yotam Nitzan, Eli Shechtman, Dani Lischinski
Several works already utilize some basic properties of aligned StyleGAN models to perform image-to-image translation.
1 code implementation • CVPR 2022 • Yotam Nitzan, Rinon Gal, Ofir Brenner, Daniel Cohen-Or
For modern generative frameworks, this semantic encoding manifests as smooth, linear directions which affect image attributes in a disentangled manner.
8 code implementations • 4 Feb 2021 • Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, Daniel Cohen-Or
We then 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.
10 code implementations • CVPR 2021 • Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, Daniel Cohen-Or
We present a generic image-to-image translation framework, pixel2style2pixel (pSp).
3 code implementations • 15 May 2020 • Yotam Nitzan, Amit Bermano, Yangyan Li, Daniel Cohen-Or
Learning disentangled representations of data is a fundamental problem in artificial intelligence.