7 citations · 18 across the 8 of their papers we have counts for
9 papers
Versatile Editing of Video Content, Actions, and Dynamics without Training
Vladimir Kulikov, Roni Paiss, Andrey Voynov +3
Controlled video generation has seen drastic improvements in recent years. However, editing actions and dynamic events, or inserting contents that should affect the behaviors of ot…
MotionV2V: Editing Motion in a Video
Ryan Burgert, Charles Herrmann, Forrester Cole +4
While generative video models have achieved remarkable fidelity and consistency, applying these capabilities to video editing remains a complex challenge. Recent research has explo…
Visual Diffusion Models are Geometric Solvers
Nir Goren, Shai Yehezkel, Omer Dahary +3
In this paper we show that visual diffusion models can serve as effective geometric solvers: they can directly reason about geometric problems by working in pixel space. We first d…
Navigating with Annealing Guidance Scale in Diffusion Space
Shai Yehezkel, Omer Dahary, Andrey Voynov +1
Denoising diffusion models excel at generating high-quality images conditioned on text prompts, yet their effectiveness heavily relies on careful guidance during the sampling proce…
ReNoise: Real Image Inversion Through Iterative Noising
Daniel Garibi, Or Patashnik, Andrey Voynov +2
Recent advancements in text-guided diffusion models have unlocked powerful image manipulation capabilities. However, applying these methods to real images necessitates the inversio…
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…