6 citations · 8 across the 7 of their papers we have counts for
7 papers
LCM-Lookahead for Encoder-based Text-to-Image Personalization
Rinon Gal, Or Lichter, Elad Richardson +4
Recent advancements in diffusion models have introduced fast sampling methods that can effectively produce high-quality images in just one or a few denoising steps. Interestingly,…
MyVLM: Personalizing VLMs for User-Specific Queries
Yuval Alaluf, Elad Richardson, Sergey Tulyakov +2
Recent large-scale vision-language models (VLMs) have demonstrated remarkable capabilities in understanding and generating textual descriptions for visual content. However, these m…
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…
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…
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…
3D Face Reconstruction by Learning from Synthetic Data
Elad Richardson, Matan Sela, Ron Kimmel
Fast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications. Here, we introduce a learning-b…