11 citations · 40 across the 6 of their papers we have counts for
10 papers
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
Gal Metzer, Elad Richardson, Or Patashnik +2
Text-guided image generation has progressed rapidly in recent years, inspiring major breakthroughs in text-guided shape generation. Recently, it has been shown that using score dis…
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…
FEAT: Face Editing with Attention
Xianxu Hou, Linlin Shen, Or Patashnik +2
Employing the latent space of pretrained generators has recently been shown to be an effective means for GAN-based face manipulation. The success of this approach heavily relies on…
Third Time's the Charm? Image and Video Editing with StyleGAN3
Yuval Alaluf, Or Patashnik, Zongze Wu +4
StyleGAN is arguably one of the most intriguing and well-studied generative models, demonstrating impressive performance in image generation, inversion, and manipulation. In this w…
StyleFusion: A Generative Model for Disentangling Spatial Segments
Omer Kafri, Or Patashnik, Yuval Alaluf +1
We present StyleFusion, a new mapping architecture for StyleGAN, which takes as input a number of latent codes and fuses them into a single style code. Inserting the resulting styl…
ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement
Yuval Alaluf, Or Patashnik, Daniel Cohen-Or
Recently, the power of unconditional image synthesis has significantly advanced through the use of Generative Adversarial Networks (GANs). The task of inverting an image into its c…