17 citations · 25 across the 45 of their papers we have counts for
16 papers · 1 filter
In-Context Sync-LoRA for Portrait Video Editing
Sagi Polaczek, Or Patashnik, Ali Mahdavi-Amiri +1
Editing portrait videos is a challenging task that requires flexible yet precise control over a wide range of modifications, such as appearance changes, expression edits, or the ad…
SAEdit: Token-level control for continuous image editing via Sparse AutoEncoder
Ronen Kamenetsky, Sara Dorfman, Daniel Garibi +3
Large-scale text-to-image diffusion models have become the backbone of modern image editing, yet text prompts alone do not offer adequate control over the editing process. Two prop…
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…
Kontinuous Kontext: Continuous Strength Control for Instruction-based Image Editing
Rishubh Parihar, Or Patashnik, Daniil Ostashev +3
Instruction-based image editing offers a powerful and intuitive way to manipulate images through natural language. Yet, relying solely on text instructions limits fine-grained cont…
Zero-Shot Dynamic Concept Personalization with Grid-Based LoRA
Rameen Abdal, Or Patashnik, Ekaterina Deyneka +5
Recent advances in text-to-video generation have enabled high-quality synthesis from text and image prompts. While the personalization of dynamic concepts, which capture subject-sp…
EditP23: 3D Editing via Propagation of Image Prompts to Multi-View
Roi Bar-On, Dana Cohen-Bar, Daniel Cohen-Or
We present EditP23, a method for mask-free 3D editing that propagates 2D image edits to multi-view representations in a 3D-consistent manner. In contrast to traditional approaches…
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…
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…
Be Decisive: Noise-Induced Layouts for Multi-Subject Generation
Omer Dahary, Yehonathan Cohen, Or Patashnik +2
Generating multiple distinct subjects remains a challenge for existing text-to-image diffusion models. Complex prompts often lead to subject leakage, causing inaccuracies in quanti…
TriTex: Learning Texture from a Single Mesh via Triplane Semantic Features
Dana Cohen-Bar, Daniel Cohen-Or, Gal Chechik +1
As 3D content creation continues to grow, transferring semantic textures between 3D meshes remains a significant challenge in computer graphics. While recent methods leverage text-…
Tight Inversion: Image-Conditioned Inversion for Real Image Editing
Edo Kadosh, Nir Goren, Or Patashnik +2
Text-to-image diffusion models offer powerful image editing capabilities. To edit real images, many methods rely on the inversion of the image into Gaussian noise. A common approac…
Dynamic Concepts Personalization from Single Videos
Rameen Abdal, Or Patashnik, Ivan Skorokhodov +5
Personalizing generative text-to-image models has seen remarkable progress, but extending this personalization to text-to-video models presents unique challenges. Unlike static con…
IP-Composer: Semantic Composition of Visual Concepts
Sara Dorfman, Dana Cohen-Bar, Rinon Gal +1
Content creators often draw inspiration from multiple visual sources, combining distinct elements to craft new compositions. Modern computational approaches now aim to emulate this…
AnyTop: Character Animation Diffusion with Any Topology
Inbar Gat, Sigal Raab, Guy Tevet +3
Generating motion for arbitrary skeletons is a longstanding challenge in computer graphics, remaining largely unexplored due to the scarcity of diverse datasets and the irregular n…
Object-level Visual Prompts for Compositional Image Generation
Gaurav Parmar, Or Patashnik, Kuan-Chieh Wang +5
We introduce a method for composing object-level visual prompts within a text-to-image diffusion model. Our approach addresses the task of generating semantically coherent composit…
Nested Attention: Semantic-aware Attention Values for Concept Personalization
Or Patashnik, Rinon Gal, Daniil Ostashev +3
Personalizing text-to-image models to generate images of specific subjects across diverse scenes and styles is a rapidly advancing field. Current approaches often face challenges i…