6 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…
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,…