2 citations · 3 across the 7 of their papers we have counts for
13 papers · 1 filter
Bootstrap Your Generator: Unpaired Visual Editing with Flow Matching
Yoad Tewel, Yuval Atzmon, Gal Chechik +1
Modern generative models possess a deep understanding of visual content, yet training them for image editing typically requires massive datasets of paired examples. This limits sca…
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…
Data-Driven Loss Functions for Inference-Time Optimization in Text-to-Image
Sapir Esther Yiflach, Yuval Atzmon, Gal Chechik
Text-to-image diffusion models can generate stunning visuals, yet they often fail at tasks children find trivial--like placing a dog to the right of a teddy bear rather than to the…
Motion by Queries: Identity-Motion Trade-offs in Text-to-Video Generation
Yuval Atzmon, Rinon Gal, Yoad Tewel +2
Text-to-video diffusion models have shown remarkable progress in generating coherent video clips from textual descriptions. However, the interplay between motion, structure, and id…
Lay-A-Scene: Personalized 3D Object Arrangement Using Text-to-Image Priors
Ohad Rahamim, Hilit Segev, Idan Achituve +3
Generating 3D visual scenes is at the forefront of visual generative AI, but current 3D generation techniques struggle with generating scenes with multiple high-resolution objects.…
Training-Free Consistent Text-to-Image Generation
Yoad Tewel, Omri Kaduri, Rinon Gal +4
Text-to-image models offer a new level of creative flexibility by allowing users to guide the image generation process through natural language. However, using these models to cons…