O
Omer Tov
Researcher at Tel Aviv University
Publications - 7
Citations - 471
Omer Tov 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 1, co-authored 3 publications receiving 30 citations.
Papers
More filters
Journal ArticleDOI
Imagic: Text-Based Real Image Editing with Diffusion Models
Bahjat Kawar,Shiran Zada,Oran Lang,Omer Tov,Hui-Tang Chang,Tali Dekel,Inbar Mosseri,Michal Irani +7 more
TL;DR: This paper demonstrates, for the very first time, the ability to apply complex (e.g., non-rigid) text-guided semantic edits to a single real image using a pretrained text-to-image diffusion model.
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
State‐of‐the‐Art in the Architecture, Methods and Applications of StyleGAN
Amit Bermano,Rinon Gal,Yuval Alaluf,Ron Mokady,Yotam Nitzan,Omer Tov,Or Patashnik,Daniel Cohen-Or +7 more
TL;DR: This state‐of‐the‐art report covers the StyleGAN architecture, and the ways it has been employed since its conception, while also analyzing its severe limitations.
Posted Content
HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing
TL;DR: HyperStyle as discussed by the authors learns to modulate StyleGAN's weights to faithfully express a given image in editable regions of the latent space, which yields reconstructions comparable to those of optimization techniques with the near real-time inference capabilities of encoders.
Proceedings ArticleDOI
Self-Distilled StyleGAN: Towards Generation from Internet Photos
Ron Mokady,Michal Yarom,Omer Tov,Oran Lang,Daniel Cohen-Or,Tali Dekel,Michal Irani,Inbar Mosseri +7 more
TL;DR: This paper shows how StyleGAN can be adapted to work on raw uncurated images collected from the Internet, and proposes a StyleGAN-based self-distillation approach, which enables the generation of high-quality images, while minimizing the loss in diversity of the data.