3 citations · 3 across the 6 of their papers we have counts for
6 papers
Token-to-Token Alignment of Text Embeddings for Semantic Blending
Saar Huberman, Ron Mokady, Or Patashnik +1
In modern generative models, images are specified and controlled through text prompts. In practice, images are generated from sequences of tokens derived from these prompts. Howeve…
Deep Accurate Solver for the Geodesic Problem
Saar Huberman, Amit Bracha, Ron Kimmel
A common approach to compute distances on continuous surfaces is by considering a discretized polygonal mesh approximating the surface and estimating distances on the polygon. We s…
BBQ-to-Image: Numeric Bounding Box and Qolor Control in Large-Scale Text-to-Image Models
Eliran Kachlon, Alexander Visheratin, Nimrod Sarid +6
Text-to-image models have rapidly advanced in realism and controllability, with recent approaches leveraging long, detailed captions to support fine-grained generation. However, a…
SemanticMoments: Training-Free Motion Similarity via Third Moment Features
Saar Huberman, Kfir Goldberg, Or Patashnik +2
Retrieving videos based on semantic motion is a fundamental, yet unsolved, problem. Existing video representation approaches overly rely on static appearance and scene context rath…
Generating an Image From 1,000 Words: Enhancing Text-to-Image With Structured Captions
Eyal Gutflaish, Eliran Kachlon, Hezi Zisman +8
Text-to-image models have rapidly evolved from casual creative tools to professional-grade systems, achieving unprecedented levels of image quality and realism. Yet, most models ar…
Image Generation from Contextually-Contradictory Prompts
Saar Huberman, Or Patashnik, Omer Dahary +2
Text-to-image diffusion models excel at generating high-quality, diverse images from natural language prompts. However, they often fail to produce semantically accurate results whe…