704 citations · 4.1k across the 112 of their papers we have counts for
128 papers
In-2-4D: Inbetweening from Two Single-View Images to 4D Generation
Sauradip Nag, Daniel Cohen-Or, Hao Zhang +1
We pose a new problem, In-2-4D, for generative 4D (i.e., 3D + motion) inbetweening to interpolate two single-view images. In contrast to video/4D generation from only text or a sin…
EXIM: A Hybrid Explicit-Implicit Representation for Text-Guided 3D Shape Generation
Zhengzhe Liu, Jingyu Hu, Ka-Hei Hui +3
This paper presents a new text-guided technique for generating 3D shapes. The technique leverages a hybrid 3D shape representation, namely EXIM, combining the strengths of explicit…
CLiC: Concept Learning in Context
Mehdi Safaee, Aryan Mikaeili, Or Patashnik +2
This paper addresses the challenge of learning a local visual pattern of an object from one image, and generating images depicting objects with that pattern. Learning a localized c…
Cross-Image Attention for Zero-Shot Appearance Transfer
Yuval Alaluf, Daniel Garibi, Or Patashnik +2
Recent advancements in text-to-image generative models have demonstrated a remarkable ability to capture a deep semantic understanding of images. In this work, we leverage this sem…
Noise-Free Score Distillation
Oren Katzir, Or Patashnik, Daniel Cohen-Or +1
Score Distillation Sampling (SDS) has emerged as the de facto approach for text-to-content generation in non-image domains. In this paper, we reexamine the SDS process and introduc…
MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion
Roy Kapon, Guy Tevet, Daniel Cohen-Or +1
We introduce Multi-view Ancestral Sampling (MAS), a method for 3D motion generation, using 2D diffusion models that were trained on motions obtained from in-the-wild videos. As suc…
EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes
Jingyuan Yang, Qirui Huang, Tingting Ding +3
Visual Emotion Analysis (VEA) aims at predicting people's emotional responses to visual stimuli. This is a promising, yet challenging, task in affective computing, which has drawn…
Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models
Moab Arar, Rinon Gal, Yuval Atzmon +4
Text-to-image (T2I) personalization allows users to guide the creative image generation process by combining their own visual concepts in natural language prompts. Recently, encode…
Facial Reenactment Through a Personalized Generator
Ariel Elazary, Yotam Nitzan, Daniel Cohen-Or
In recent years, the role of image generative models in facial reenactment has been steadily increasing. Such models are usually subject-agnostic and trained on domain-wide dataset…
SVNR: Spatially-variant Noise Removal with Denoising Diffusion
Naama Pearl, Yaron Brodsky, Dana Berman +4
Denoising diffusion models have recently shown impressive results in generative tasks. By learning powerful priors from huge collections of training images, such models are able to…
SENS: Part-Aware Sketch-based Implicit Neural Shape Modeling
Alexandre Binninger, Amir Hertz, Olga Sorkine-Hornung +2
We present SENS, a novel method for generating and editing 3D models from hand-drawn sketches, including those of abstract nature. Our method allows users to quickly and easily ske…
Concept Decomposition for Visual Exploration and Inspiration
Yael Vinker, Andrey Voynov, Daniel Cohen-Or +1
A creative idea is often born from transforming, combining, and modifying ideas from existing visual examples capturing various concepts. However, one cannot simply copy the concep…
A Neural Space-Time Representation for Text-to-Image Personalization
Yuval Alaluf, Elad Richardson, Gal Metzer +1
A key aspect of text-to-image personalization methods is the manner in which the target concept is represented within the generative process. This choice greatly affects the visual…
Break-A-Scene: Extracting Multiple Concepts from a Single Image
Omri Avrahami, Kfir Aberman, Ohad Fried +2
Text-to-image model personalization aims to introduce a user-provided concept to the model, allowing its synthesis in diverse contexts. However, current methods primarily focus on…
Delta Denoising Score
Amir Hertz, Kfir Aberman, Daniel Cohen-Or
We introduce Delta Denoising Score (DDS), a novel scoring function for text-based image editing that guides minimal modifications of an input image towards the content described in…
Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes
Dana Cohen-Bar, Elad Richardson, Gal Metzer +2
Recent breakthroughs in text-guided image generation have led to remarkable progress in the field of 3D synthesis from text. By optimizing neural radiance fields (NeRF) directly fr…
Word-As-Image for Semantic Typography
Shir Iluz, Yael Vinker, Amir Hertz +3
A word-as-image is a semantic typography technique where a word illustration presents a visualization of the meaning of the word, while also preserving its readability. We present…
Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models
Rinon Gal, Moab Arar, Yuval Atzmon +3
Text-to-image personalization aims to teach a pre-trained diffusion model to reason about novel, user provided concepts, embedding them into new scenes guided by natural language p…
Localizing Object-level Shape Variations with Text-to-Image Diffusion Models
Or Patashnik, Daniel Garibi, Idan Azuri +2
Text-to-image models give rise to workflows which often begin with an exploration step, where users sift through a large collection of generated images. The global nature of the te…
SKED: Sketch-guided Text-based 3D Editing
Aryan Mikaeili, Or Perel, Mehdi Safaee +2
Text-to-image diffusion models are gradually introduced into computer graphics, recently enabling the development of Text-to-3D pipelines in an open domain. However, for interactiv…
P+: Extended Textual Conditioning in Text-to-Image Generation
Andrey Voynov, Qinghao Chu, Daniel Cohen-Or +1
We introduce an Extended Textual Conditioning space in text-to-image models, referred to as . This space consists of multiple textual conditions, derived from per-layer prompts…
TEXTure: Text-Guided Texturing of 3D Shapes
Elad Richardson, Gal Metzer, Yuval Alaluf +2
In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion mode…
Cross-domain Compositing with Pretrained Diffusion Models
Roy Hachnochi, Mingrui Zhao, Nadav Orzech +4
Diffusion models have enabled high-quality, conditional image editing capabilities. We propose to expand their arsenal, and demonstrate that off-the-shelf diffusion models can be u…
Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Models
Hila Chefer, Yuval Alaluf, Yael Vinker +2
Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt. While revolutionary, curre…