most citedUnsupervised Cross-Domain Image Generation

429 citations · 429 across the 2 of their papers we have counts for

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6 papers · 1 filter

cs.CV2024

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…

cs.CV20232 cited

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…

cs.CV2023

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…

cs.CV202323 cited

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…

cs.CV2021

Locally Shifted Attention With Early Global Integration

Shelly Sheynin, Sagie Benaim, Adam Polyak +1

Recent work has shown the potential of transformers for computer vision applications. An image is first partitioned into patches, which are then used as input tokens for the attent…

cs.CV2016429 cited

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…