41 citations · 51 across the 8 of their papers we have counts for
7 papers · 1 filter
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…