53 citations · 140 across the 7 of their papers we have counts for
10 papers
Shape-Pose Disentanglement using SE(3)-equivariant Vector Neurons
Oren Katzir, Dani Lischinski, Daniel Cohen-Or
We introduce an unsupervised technique for encoding point clouds into a canonical shape representation, by disentangling shape and pose. Our encoder is stable and consistent, meani…
Multi-level Latent Space Structuring for Generative Control
Oren Katzir, Vicky Perepelook, Dani Lischinski +1
Truncation is widely used in generative models for improving the quality of the generated samples, at the expense of reducing their diversity. We propose to leverage the StyleGAN g…
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…
StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
Or Patashnik, Zongze Wu, Eli Shechtman +2
Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of Style…
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation
Zongze Wu, Dani Lischinski, Eli Shechtman
We explore and analyze the latent style space of StyleGAN2, a state-of-the-art architecture for image generation, using models pretrained on several different datasets. We first sh…
Differentiable Refraction-Tracing for Mesh Reconstruction of Transparent Objects
Jiahui Lyu, Bojian Wu, Dani Lischinski +2
Capturing the 3D geometry of transparent objects is a challenging task, ill-suited for general-purpose scanning and reconstruction techniques, since these cannot handle specular li…