41 citations · 51 across the 8 of their papers we have counts for
10 papers
VideoJAM: Joint Appearance-Motion Representations for Enhanced Motion Generation in Video Models
Hila Chefer, Uriel Singer, Amit Zohar +5
Despite tremendous recent progress, generative video models still struggle to capture real-world motion, dynamics, and physics. We show that this limitation arises from the convent…
Through-The-Mask: Mask-based Motion Trajectories for Image-to-Video Generation
Guy Yariv, Yuval Kirstain, Amit Zohar +5
We consider the task of Image-to-Video (I2V) generation, which involves transforming static images into realistic video sequences based on a textual description. While recent advan…
Movie Gen: A Cast of Media Foundation Models
Adam Polyak, Amit Zohar, Andrew Brown +85
We present Movie Gen, a cast of foundation models that generates high-quality, 1080p HD videos with different aspect ratios and synchronized audio. We also show additional capabili…
Video Editing via Factorized Diffusion Distillation
Uriel Singer, Amit Zohar, Yuval Kirstain +4
We introduce Emu Video Edit (EVE), a model that establishes a new state-of-the art in video editing without relying on any supervised video editing data. To develop EVE we separate…
Emu Edit: Precise Image Editing via Recognition and Generation Tasks
Shelly Sheynin, Adam Polyak, Uriel Singer +5
Instruction-based image editing holds immense potential for a variety of applications, as it enables users to perform any editing operation using a natural language instruction. Ho…
Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation
Yuval Kirstain, Adam Polyak, Uriel Singer +3
The ability to collect a large dataset of human preferences from text-to-image users is usually limited to companies, making such datasets inaccessible to the public. To address th…
X&Fuse: Fusing Visual Information in Text-to-Image Generation
Yuval Kirstain, Omer Levy, Adam Polyak
We introduce X&Fuse, a general approach for conditioning on visual information when generating images from text. We demonstrate the potential of X&Fuse in three different text-to-i…
A Few More Examples May Be Worth Billions of Parameters
Yuval Kirstain, Patrick Lewis, Sebastian Riedel +1
We investigate the dynamics of increasing the number of model parameters versus the number of labeled examples across a wide variety of tasks. Our exploration reveals that while sc…
Few-Shot Question Answering by Pretraining Span Selection
Ori Ram, Yuval Kirstain, Jonathan Berant +2
In several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order of 100,000 annotated questions and answers. We explore the mor…
Coreference Resolution without Span Representations
Yuval Kirstain, Ori Ram, Omer Levy
The introduction of pretrained language models has reduced many complex task-specific NLP models to simple lightweight layers. An exception to this trend is coreference resolution,…