704 citations · 4.1k across the 112 of their papers we have counts for
23 papers · 2 filters
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…
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…
Domain Expansion of Image Generators
Yotam Nitzan, Michaël Gharbi, Richard Zhang +4
Can one inject new concepts into an already trained generative model, while respecting its existing structure and knowledge? We propose a new task - domain expansion - to address t…