4 citations · 14 across the 20 of their papers we have counts for
21 papers · 1 filter
Stuck on Shuffle: On Attribute-Object Binding in Vision-Language Models
Netanel Y. Tamir, Michael Green, Issar Tzachor +4
Retrieval is a fundamental task in multimodal systems: given an image or text query, the goal is to find the most relevant images or texts in a collection, often using a shared emb…
PixReenact: Pixel-Conditioned Causal Video Diffusion for Streaming Head-Avatar Reenactment
Gavriel Habib, Dvir Samuel, Or Shimshi +1
Streaming head-avatar reenactment aims to animate a reference image according to a live driving video, requiring robust motion transfer, long-term identity stability, and low laten…
All modalities are equal, but video is more equal: Closing the Cross-Attention Gap in Joint Video Generation
Ohad Rahamim, Dvir Samuel, Idan Schwartz +1
Video is a rich representation of a physical event, capturing appearance, geometry, motion, and temporal evolution. Other modalities, such as 3D body motion or audio, encode narrow…
UniProbe: A Learnable Token-Level Hallucination Detector for Large VLMs using Multi-Structural Internal Representations
Dvir Samuel, Guy Bar-Shalom, Fabrizio Frasca +4
Large Vision-Language Models (LVLMs) achieve impressive visual reasoning and dialogue capabilities, yet frequently hallucinate content unsupported by the visual input. Effective mi…
WarpHammer: Densifying Scene Warps with 3D Object Priors for Extreme View Synthesis
Michael Green, Gavriel Habib, Dvir Samuel +4
Projection-conditioned novel view synthesis (NVS) warps an explicit 3D reconstruction of the input view into the target camera and conditions a generator on the warped rendering. T…
LiveSVG: Zero-Shot SVG Animation via Video Generation
Matan Levy, Ran Margolin, Bar Cavia +6
We introduce LiveSVG, a zero-shot approach for generating Scalable Vector Graphics (SVG) animations using video diffusion models. Current SVG animation methods struggle with comple…
Fast 4D Mesh Generation by Spatio-Temporal Attention Chains
Dvir Samuel, Yuval Atzmon, Gal Chechik +1
4D mesh generation has recently emerged as a powerful paradigm for recovering dynamic 3D structure from videos, but existing methods remain slow, computationally expensive, and dif…
Retrieval-Augmented Gaussian Avatars: Improving Expression Generalization
Matan Levy, Gavriel Habib, Issar Tzachor +5
Template-free animatable head avatars can achieve high visual fidelity by learning expression-dependent facial deformation directly from a subject's capture, avoiding parametric fa…
VidVec: Unlocking Video MLLM Embeddings for Video-Text Retrieval
Issar Tzachor, Dvir Samuel, Rami Ben-Ari
Recent studies have adapted generative Multimodal Large Language Models (MLLMs) into embedding extractors for vision tasks, typically through fine-tuning to produce universal repre…
Fast Autoregressive Video Diffusion and World Models with Temporal Cache Compression and Sparse Attention
Dvir Samuel, Issar Tzachor, Matan Levy +3
Autoregressive video diffusion models enable streaming generation, opening the door to long-form synthesis, video world models, and interactive neural game engines. However, their…
Story2Board: A Training-Free Approach for Expressive Storyboard Generation
David Dinkevich, Matan Levy, Omri Avrahami +2
We present Story2Board, a training-free framework for expressive storyboard generation from natural language. Existing methods narrowly focus on subject identity, overlooking key a…
Per-Query Visual Concept Learning
Ori Malca, Dvir Samuel, Gal Chechik
Visual concept learning, also known as Text-to-image personalization, is the process of teaching new concepts to a pretrained model. This has numerous applications from product pla…
OmnimatteZero: Fast Training-free Omnimatte with Pre-trained Video Diffusion Models
Dvir Samuel, Matan Levy, Nir Darshan +2
In Omnimatte, one aims to decompose a given video into semantically meaningful layers, including the background and individual objects along with their associated effects, such as…
Find your Needle: Small Object Image Retrieval via Multi-Object Attention Optimization
Michael Green, Matan Levy, Issar Tzachor +3
We address the challenge of Small Object Image Retrieval (SoIR), where the goal is to retrieve images containing a specific small object, in a cluttered scene. The key challenge in…
Task-Specific Adaptation with Restricted Model Access
Matan Levy, Rami Ben-Ari, Dvir Samuel +2
The emergence of foundational models has greatly improved performance across various downstream tasks, with fine-tuning often yielding even better results. However, existing fine-t…
Bringing Objects to Life: training-free 4D generation from 3D objects through view consistent noise
Ohad Rahamim, Ori Malca, Dvir Samuel +1
Recent advancements in generative models have enabled the creation of dynamic 4D content - 3D objects in motion - based on text prompts, which holds potential for applications in v…
EffoVPR: Effective Foundation Model Utilization for Visual Place Recognition
Issar Tzachor, Boaz Lerner, Matan Levy +8
The task of Visual Place Recognition (VPR) is to predict the location of a query image from a database of geo-tagged images. Recent studies in VPR have highlighted the significant…
Where's Waldo: Diffusion Features for Personalized Segmentation and Retrieval
Dvir Samuel, Rami Ben-Ari, Matan Levy +2
Personalized retrieval and segmentation aim to locate specific instances within a dataset based on an input image and a short description of the reference instance. While supervise…
Lightning-Fast Image Inversion and Editing for Text-to-Image Diffusion Models
Dvir Samuel, Barak Meiri, Haggai Maron +5
Diffusion inversion is the problem of taking an image and a text prompt that describes it and finding a noise latent that would generate the exact same image. Most current determin…
Norm-guided latent space exploration for text-to-image generation
Dvir Samuel, Rami Ben-Ari, Nir Darshan +2
Text-to-image diffusion models show great potential in synthesizing a large variety of concepts in new compositions and scenarios. However, the latent space of initial seeds is sti…
Generating images of rare concepts using pre-trained diffusion models
Dvir Samuel, Rami Ben-Ari, Simon Raviv +2
Text-to-image diffusion models can synthesize high-quality images, but they have various limitations. Here we highlight a common failure mode of these models, namely, generating un…