65 citations · 163 across the 8 of their papers we have counts for
8 papers · 1 filter
DynVFX: Augmenting Real Videos with Dynamic Content
Danah Yatim, Rafail Fridman, Omer Bar-Tal +1
We present a method for augmenting real-world videos with newly generated dynamic content. Given an input video and a simple user-provided text instruction describing the desired c…
Lumiere: A Space-Time Diffusion Model for Video Generation
Omer Bar-Tal, Hila Chefer, Omer Tov +14
We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthes…
Space-Time Diffusion Features for Zero-Shot Text-Driven Motion Transfer
Danah Yatim, Rafail Fridman, Omer Bar-Tal +2
We present a new method for text-driven motion transfer - synthesizing a video that complies with an input text prompt describing the target objects and scene while maintaining an…
Disentangling Structure and Appearance in ViT Feature Space
Narek Tumanyan, Omer Bar-Tal, Shir Amir +2
We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source…
TokenFlow: Consistent Diffusion Features for Consistent Video Editing
Michal Geyer, Omer Bar-Tal, Shai Bagon +1
The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality a…
MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
Omer Bar-Tal, Lior Yariv, Yaron Lipman +1
Recent advances in text-to-image generation with diffusion models present transformative capabilities in image quality. However, user controllability of the generated image, and fa…
Text2LIVE: Text-Driven Layered Image and Video Editing
Omer Bar-Tal, Dolev Ofri-Amar, Rafail Fridman +2
We present a method for zero-shot, text-driven appearance manipulation in natural images and videos. Given an input image or video and a target text prompt, our goal is to edit the…
Splicing ViT Features for Semantic Appearance Transfer
Narek Tumanyan, Omer Bar-Tal, Shai Bagon +1
We present a method for semantically transferring the visual appearance of one natural image to another. Specifically, our goal is to generate an image in which objects in a source…