472 citations · 526 across the 2 of their papers we have counts for
5 papers
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.…
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…
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…
An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon +4
Text-to-image models offer unprecedented freedom to guide creation through natural language. Yet, it is unclear how such freedom can be exercised to generate images of specific uni…
Learning to generalize to new compositions in image understanding
Yuval Atzmon, Jonathan Berant, Vahid Kezami +2
Recurrent neural networks have recently been used for learning to describe images using natural language. However, it has been observed that these models generalize poorly to scene…