42 citations · 55 across the 7 of their papers we have counts for
7 papers
Solaris: Towards Interfaces That Are Generated, Not Coded
Yuval Alaluf, Omri Avrahami, Guy Bukchin Leshem +18
Digital interfaces are traditionally implemented through intermediate representations such as code, requiring their appearance and behavior to be specified in advance. We introduce…
Eye2Eye: A Simple Approach for Monocular-to-Stereo Video Synthesis
Michal Geyer, Omer Tov, Linyi Jin +4
The rising popularity of immersive visual experiences has increased interest in stereoscopic 3D video generation. Despite significant advances in video synthesis, creating 3D video…
Generative Omnimatte: Learning to Decompose Video into Layers
Yao-Chih Lee, Erika Lu, Sarah Rumbley +4
Given a video and a set of input object masks, an omnimatte method aims to decompose the video into semantically meaningful layers containing individual objects along with their as…
TokenFlow: Consistent Diffusion Features for Consistent Video Editing
Michal Geyer, Omer Bar-Tal, Shai Bagon +1
The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality a…
Neural Congealing: Aligning Images to a Joint Semantic Atlas
Dolev Ofri-Amar, Michal Geyer, Yoni Kasten +1
We present Neural Congealing -- a zero-shot self-supervised framework for detecting and jointly aligning semantically-common content across a given set of images. Our approach harn…
Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation
Narek Tumanyan, Michal Geyer, Shai Bagon +1
Large-scale text-to-image generative models have been a revolutionary breakthrough in the evolution of generative AI, allowing us to synthesize diverse images that convey highly co…
Minimum Description Length Recurrent Neural Networks
Nur Lan, Michal Geyer, Emmanuel Chemla +1
We train neural networks to optimize a Minimum Description Length score, i.e., to balance between the complexity of the network and its accuracy at a task. We show that networks op…