587 citations · 660 across the 14 of their papers we have counts for
20 papers
Versatile Editing of Video Content, Actions, and Dynamics without Training
Vladimir Kulikov, Roni Paiss, Andrey Voynov +3
Controlled video generation has seen drastic improvements in recent years. However, editing actions and dynamic events, or inserting contents that should affect the behaviors of ot…
Eye2Eye: A Simple Approach for Monocular-to-Stereo Video Synthesis
Michal Geyer, Omer Tov, Linyi Jin +4
The rising popularity of immersive visual experiences has increased interest in stereoscopic 3D video generation. Despite significant advances in video synthesis, creating 3D video…
TokenVerse: Versatile Multi-concept Personalization in Token Modulation Space
Daniel Garibi, Shahar Yadin, Roni Paiss +6
We present TokenVerse -- a method for multi-concept personalization, leveraging a pre-trained text-to-image diffusion model. Our framework can disentangle complex visual elements a…
ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning
David Junhao Zhang, Roni Paiss, Shiran Zada +7
Recently, breakthroughs in video modeling have allowed for controllable camera trajectories in generated videos. However, these methods cannot be directly applied to user-provided…
Still-Moving: Customized Video Generation without Customized Video Data
Hila Chefer, Shiran Zada, Roni Paiss +7
Customizing text-to-image (T2I) models has seen tremendous progress recently, particularly in areas such as personalization, stylization, and conditional generation. However, expan…
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…
Idempotent Generative Network
Assaf Shocher, Amil Dravid, Yossi Gandelsman +3
We propose a new approach for generative modeling based on training a neural network to be idempotent. An idempotent operator is one that can be applied sequentially without changi…
The Hidden Language of Diffusion Models
Hila Chefer, Oran Lang, Mor Geva +5
Text-to-image diffusion models have demonstrated an unparalleled ability to generate high-quality, diverse images from a textual prompt. However, the internal representations learn…
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…
Imagic: Text-Based Real Image Editing with Diffusion Models
Bahjat Kawar, Shiran Zada, Oran Lang +5
Text-conditioned image editing has recently attracted considerable interest. However, most methods are currently either limited to specific editing types (e.g., object overlay, sty…
MyStyle: A Personalized Generative Prior
Yotam Nitzan, Kfir Aberman, Qiurui He +6
We introduce MyStyle, a personalized deep generative prior trained with a few shots of an individual. MyStyle allows to reconstruct, enhance and edit images of a specific person, s…
Self-Distilled StyleGAN: Towards Generation from Internet Photos
Ron Mokady, Michal Yarom, Omer Tov +5
StyleGAN is known to produce high-fidelity images, while also offering unprecedented semantic editing. However, these fascinating abilities have been demonstrated only on a limited…
Deep Saliency Prior for Reducing Visual Distraction
Kfir Aberman, Junfeng He, Yossi Gandelsman +5
Using only a model that was trained to predict where people look at images, and no additional training data, we can produce a range of powerful editing effects for reducing distrac…
Explaining in Style: Training a GAN to explain a classifier in StyleSpace
Oran Lang, Yossi Gandelsman, Michal Yarom +8
Image classification models can depend on multiple different semantic attributes of the image. An explanation of the decision of the classifier needs to both discover and visualize…
SpeedNet: Learning the Speediness in Videos
Sagie Benaim, Ariel Ephrat, Oran Lang +5
We wish to automatically predict the "speediness" of moving objects in videos---whether they move faster, at, or slower than their "natural" speed. The core component in our approa…
Semantic Pyramid for Image Generation
Assaf Shocher, Yossi Gandelsman, Inbar Mosseri +4
We present a novel GAN-based model that utilizes the space of deep features learned by a pre-trained classification model. Inspired by classical image pyramid representations, we c…
Speech2Face: Learning the Face Behind a Voice
Tae-Hyun Oh, Tali Dekel, Changil Kim +4
How much can we infer about a person's looks from the way they speak? In this paper, we study the task of reconstructing a facial image of a person from a short audio recording of…
Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation
Ariel Ephrat, Inbar Mosseri, Oran Lang +5
We present a joint audio-visual model for isolating a single speech signal from a mixture of sounds such as other speakers and background noise. Solving this task using only audio…
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
Amélie Royer, Konstantinos Bousmalis, Stephan Gouws +4
Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latte…
Synthesizing Normalized Faces from Facial Identity Features
Forrester Cole, David Belanger, Dilip Krishnan +3
We present a method for synthesizing a frontal, neutral-expression image of a person's face given an input face photograph. This is achieved by learning to generate facial landmark…