activity
20192022
most citedState-of-the-Art in the Architecture, Methods and Applications of StyleGAN

9 citations · 20 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV20229 cited

State-of-the-Art in the Architecture, Methods and Applications of StyleGAN

Amit H. Bermano, Rinon Gal, Yuval Alaluf +5

Generative Adversarial Networks (GANs) have established themselves as a prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating case study, owing to its rema…

cs.CV2022

Self-Distilled StyleGAN: Towards Generation from Internet Photos

Ron Mokady, Michal Yarom, Omer Tov +5

StyleGAN is known to produce high-fidelity images, while also offering unprecedented semantic editing. However, these fascinating abilities have been demonstrated only on a limited…

cs.CV20223 cited

Stitch it in Time: GAN-Based Facial Editing of Real Videos

Rotem Tzaban, Ron Mokady, Rinon Gal +2

The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing. However, replicating their succe…

cs.CV20214 cited

JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting

Ron Mokady, Rotem Tzaban, Sagie Benaim +2

The task of unsupervised motion retargeting in videos has seen substantial advancements through the use of deep neural networks. While early works concentrated on specific object p…

cs.CV20214 cited

Pivotal Tuning for Latent-based Editing of Real Images

Daniel Roich, Ron Mokady, Amit H. Bermano +1

Recently, a surge of advanced facial editing techniques have been proposed that leverage the generative power of a pre-trained StyleGAN. To successfully edit an image this way, one…

cs.CV2020

Structural-analogy from a Single Image Pair

Sagie Benaim, Ron Mokady, Amit Bermano +2

The task of unsupervised image-to-image translation has seen substantial advancements in recent years through the use of deep neural networks. Typically, the proposed solutions lea…