19 citations · 38 across the 7 of their papers we have counts for
7 papers
NoisePrints: Distortion-Free Watermarks for Authorship in Private Diffusion Models
Nir Goren, Oren Katzir, Abhinav Nakarmi +3
With the rapid adoption of diffusion models for visual content generation, proving authorship and protecting copyright have become critical. This challenge is particularly importan…
Noise-Free Score Distillation
Oren Katzir, Or Patashnik, Daniel Cohen-Or +1
Score Distillation Sampling (SDS) has emerged as the de facto approach for text-to-content generation in non-image domains. In this paper, we reexamine the SDS process and introduc…
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…
Cross-Domain Cascaded Deep Feature Translation
Oren Katzir, Dani Lischinski, Daniel Cohen-Or
In recent years we have witnessed tremendous progress in unpaired image-to-image translation methods, propelled by the emergence of DNNs and adversarial training strategies. Howeve…
CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition
Nadav Schor, Oren Katzir, Hao Zhang +1
Data-driven generative modeling has made remarkable progress by leveraging the power of deep neural networks. A reoccurring challenge is how to enable a model to generate a rich va…
DiDA: Disentangled Synthesis for Domain Adaptation
Jinming Cao, Oren Katzir, Peng Jiang +4
Unsupervised domain adaptation aims at learning a shared model for two related, but not identical, domains by leveraging supervision from a source domain to an unsupervised target…