752 citations · 1.5k across the 33 of their papers we have counts for
35 papers
Iconix: Controlling Semantics and Style in Progressive Icon Grids Generation
Zhida Sun, Xiaodong Wang, Zhenyao Zhang +4
Visual communication often needs stylistically consistent icons that span concrete and abstract meanings, for use in diverse contexts. We present Iconix, a human-AI co-creative sys…
DiffTex: Differentiable Texturing for Architectural Proxy Models
Weidan Xiong, Yongli Wu, Bochuan Zeng +4
Simplified proxy models are commonly used to represent architectural structures, reducing storage requirements and enabling real-time rendering. However, the geometric simplificati…
Creative Blends of Visual Concepts
Zhida Sun, Zhenyao Zhang, Yue Zhang +4
Visual blends combine elements from two distinct visual concepts into a single, integrated image, with the goal of conveying ideas through imaginative and often thought-provoking v…
Stable Flow: Vital Layers for Training-Free Image Editing
Omri Avrahami, Or Patashnik, Ohad Fried +4
Diffusion models have revolutionized the field of content synthesis and editing. Recent models have replaced the traditional UNet architecture with the Diffusion Transformer (DiT),…
Click2Mask: Local Editing with Dynamic Mask Generation
Omer Regev, Omri Avrahami, Dani Lischinski
Recent advancements in generative models have revolutionized image generation and editing, making these tasks accessible to non-experts. This paper focuses on local image editing,…
DiffUHaul: A Training-Free Method for Object Dragging in Images
Omri Avrahami, Rinon Gal, Gal Chechik +4
Text-to-image diffusion models have proven effective for solving many image editing tasks. However, the seemingly straightforward task of seamlessly relocating objects within a sce…
EmoEdit: Evoking Emotions through Image Manipulation
Jingyuan Yang, Jiawei Feng, Weibin Luo +3
Affective Image Manipulation (AIM) seeks to modify user-provided images to evoke specific emotional responses. This task is inherently complex due to its twofold objective: signifi…
Generating Non-Stationary Textures using Self-Rectification
Yang Zhou, Rongjun Xiao, Dani Lischinski +2
This paper addresses the challenge of example-based non-stationary texture synthesis. We introduce a novel twostep approach wherein users first modify a reference texture using sta…
GraphiMind: LLM-centric Interface for Information Graphics Design
Qirui Huang, Min Lu, Joel Lanir +3
Information graphics are pivotal in effective information dissemination and storytelling. However, creating such graphics is extremely challenging for non-professionals, since the…
Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment
Brian Gordon, Yonatan Bitton, Yonatan Shafir +5
While existing image-text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment. In this paper, we present a metho…
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Omri Avrahami, Amir Hertz, Yael Vinker +5
Recent advances in text-to-image generation models have unlocked vast potential for visual creativity. However, the users that use these models struggle with the generation of cons…
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…
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…
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…
Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields
Ori Gordon, Omri Avrahami, Dani Lischinski
Editing a local region or a specific object in a 3D scene represented by a NeRF or consistently blending a new realistic object into the scene is challenging, mainly due to the imp…
Chatting Makes Perfect: Chat-based Image Retrieval
Matan Levy, Rami Ben-Ari, Nir Darshan +1
Chats emerge as an effective user-friendly approach for information retrieval, and are successfully employed in many domains, such as customer service, healthcare, and finance. How…
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…
Data Roaming and Quality Assessment for Composed Image Retrieval
Matan Levy, Rami Ben-Ari, Nir Darshan +1
The task of Composed Image Retrieval (CoIR) involves queries that combine image and text modalities, allowing users to express their intent more effectively. However, current CoIR…
SpaText: Spatio-Textual Representation for Controllable Image Generation
Omri Avrahami, Thomas Hayes, Oran Gafni +6
Recent text-to-image diffusion models are able to generate convincing results of unprecedented quality. However, it is nearly impossible to control the shapes of different regions/…
What's in a Decade? Transforming Faces Through Time
Eric Ming Chen, Jin Sun, Apoorv Khandelwal +3
How can one visually characterize people in a decade? In this work, we assemble the Faces Through Time dataset, which contains over a thousand portrait images from each decade, spa…
Shape-Pose Disentanglement using SE(3)-equivariant Vector Neurons
Oren Katzir, Dani Lischinski, Daniel Cohen-Or
We introduce an unsupervised technique for encoding point clouds into a canonical shape representation, by disentangling shape and pose. Our encoder is stable and consistent, meani…
Multi-level Latent Space Structuring for Generative Control
Oren Katzir, Vicky Perepelook, Dani Lischinski +1
Truncation is widely used in generative models for improving the quality of the generated samples, at the expense of reducing their diversity. We propose to leverage the StyleGAN g…
Third Time's the Charm? Image and Video Editing with StyleGAN3
Yuval Alaluf, Or Patashnik, Zongze Wu +4
StyleGAN is arguably one of the most intriguing and well-studied generative models, demonstrating impressive performance in image generation, inversion, and manipulation. In this w…
Blended Diffusion for Text-driven Editing of Natural Images
Omri Avrahami, Dani Lischinski, Ohad Fried
Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural…