587 citations · 902 across the 38 of their papers we have counts for
9 papers · 1 filter
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…
State of the Art on Diffusion Models for Visual Computing
Ryan Po, Wang Yifan, Vladislav Golyanik +15
The field of visual computing is rapidly advancing due to the emergence of generative artificial intelligence (AI), which unlocks unprecedented capabilities for the generation, edi…
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…
DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data
Stephanie Fu, Netanel Tamir, Shobhita Sundaram +4
Current perceptual similarity metrics operate at the level of pixels and patches. These metrics compare images in terms of their low-level colors and textures, but fail to capture…
Neural Congealing: Aligning Images to a Joint Semantic Atlas
Dolev Ofri-Amar, Michal Geyer, Yoni Kasten +1
We present Neural Congealing -- a zero-shot self-supervised framework for detecting and jointly aligning semantically-common content across a given set of images. Our approach harn…
Teaching CLIP to Count to Ten
Roni Paiss, Ariel Ephrat, Omer Tov +4
Large vision-language models (VLMs), such as CLIP, learn rich joint image-text representations, facilitating advances in numerous downstream tasks, including zero-shot classificati…
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…
SceneScape: Text-Driven Consistent Scene Generation
Rafail Fridman, Amit Abecasis, Yoni Kasten +1
We present a method for text-driven perpetual view generation -- synthesizing long-term videos of various scenes solely, given an input text prompt describing the scene and camera…