24 citations · 57 across the 12 of their papers we have counts for
14 papers
Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
Janne Perini, Rafael Bischof, Moab Arar +4
Designing urban spaces that provide pedestrian wind comfort and safety requires time-resolved Computational Fluid Dynamics (CFD) simulations, but their current computational cost m…
Diffusion Models Are Real-Time Game Engines
Dani Valevski, Yaniv Leviathan, Moab Arar +1
We present GameNGen, the first game engine powered entirely by a neural model that also enables real-time interaction with a complex environment over long trajectories at high qual…
PALP: Prompt Aligned Personalization of Text-to-Image Models
Moab Arar, Andrey Voynov, Amir Hertz +5
Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want t…
Curved Diffusion: A Generative Model With Optical Geometry Control
Andrey Voynov, Amir Hertz, Moab Arar +2
State-of-the-art diffusion models can generate highly realistic images based on various conditioning like text, segmentation, and depth. However, an essential aspect often overlook…
The Chosen One: Consistent Characters in Text-to-Image Diffusion Models
Omri Avrahami, Amir Hertz, Yael Vinker +5
Recent advances in text-to-image generation models have unlocked vast potential for visual creativity. However, the users that use these models struggle with the generation of cons…
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…