33 citations · 52 across the 10 of their papers we have counts for
11 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…
Style Aligned Image Generation via Shared Attention
Amir Hertz, Andrey Voynov, Shlomi Fruchter +1
Large-scale Text-to-Image (T2I) models have rapidly gained prominence across creative fields, generating visually compelling outputs from textual prompts. However, controlling thes…
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…
Concept Decomposition for Visual Exploration and Inspiration
Yael Vinker, Andrey Voynov, Daniel Cohen-Or +1
A creative idea is often born from transforming, combining, and modifying ideas from existing visual examples capturing various concepts. However, one cannot simply copy the concep…
P+: Extended Textual Conditioning in Text-to-Image Generation
Andrey Voynov, Qinghao Chu, Daniel Cohen-Or +1
We introduce an Extended Textual Conditioning space in text-to-image models, referred to as . This space consists of multiple textual conditions, derived from per-layer prompts…
Sketch-Guided Text-to-Image Diffusion Models
Andrey Voynov, Kfir Aberman, Daniel Cohen-Or
Text-to-Image models have introduced a remarkable leap in the evolution of machine learning, demonstrating high-quality synthesis of images from a given text-prompt. However, these…