429 citations · 1.3k across the 24 of their papers we have counts for
15 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…
Text-To-4D Dynamic Scene Generation
Uriel Singer, Shelly Sheynin, Adam Polyak +8
We present MAV3D (Make-A-Video3D), a method for generating three-dimensional dynamic scenes from text descriptions. Our approach uses a 4D dynamic Neural Radiance Field (NeRF), whi…
SpaText: Spatio-Textual Representation for Controllable Image Generation
Omri Avrahami, Thomas Hayes, Oran Gafni +6
Recent text-to-image diffusion models are able to generate convincing results of unprecedented quality. However, it is nearly impossible to control the shapes of different regions/…
Make-A-Video: Text-to-Video Generation without Text-Video Data
Uriel Singer, Adam Polyak, Thomas Hayes +10
We propose Make-A-Video -- an approach for directly translating the tremendous recent progress in Text-to-Image (T2I) generation to Text-to-Video (T2V). Our intuition is simple: le…
KNN-Diffusion: Image Generation via Large-Scale Retrieval
Shelly Sheynin, Oron Ashual, Adam Polyak +4
Recent text-to-image models have achieved impressive results. However, since they require large-scale datasets of text-image pairs, it is impractical to train them on new domains w…
Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors
Oran Gafni, Adam Polyak, Oron Ashual +3
Recent text-to-image generation methods provide a simple yet exciting conversion capability between text and image domains. While these methods have incrementally improved the gene…
Unsupervised Creation of Parameterized Avatars
Lior Wolf, Yaniv Taigman, Adam Polyak
We study the problem of mapping an input image to a tied pair consisting of a vector of parameters and an image that is created using a graphical engine from the vector of paramete…
Unsupervised Cross-Domain Image Generation
Yaniv Taigman, Adam Polyak, Lior Wolf
We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function…
Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues
Ning Zhang, Manohar Paluri, Yaniv Taigman +2
We explore the task of recognizing peoples' identities in photo albums in an unconstrained setting. To facilitate this, we introduce the new People In Photo Albums (PIPA) dataset,…
Web-Scale Training for Face Identification
Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato +1
Scaling machine learning methods to very large datasets has attracted considerable attention in recent years, thanks to easy access to ubiquitous sensing and data from the web. We…
Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognition
Yaniv Taigman, Lior Wolf
We employ the face recognition technology developed in house at face.com to a well accepted benchmark and show that without any tuning we are able to considerably surpass state of…