752 citations · 1.5k across the 28 of their papers we have counts for
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Stable Flow: Vital Layers for Training-Free Image Editing
Omri Avrahami, Or Patashnik, Ohad Fried +4
Diffusion models have revolutionized the field of content synthesis and editing. Recent models have replaced the traditional UNet architecture with the Diffusion Transformer (DiT),…
Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfer
Sigal Raab, Inbar Gat, Nathan Sala +5
Given the remarkable results of motion synthesis with diffusion models, a natural question arises: how can we effectively leverage these models for motion editing? Existing diffusi…
Advancing Fine-Grained Classification by Structure and Subject Preserving Augmentation
Eyal Michaeli, Ohad Fried
Fine-grained visual classification (FGVC) involves classifying closely related sub-classes. This task is difficult due to the subtle differences between classes and the high intra-…
V-LASIK: Consistent Glasses-Removal from Videos Using Synthetic Data
Rotem Shalev-Arkushin, Aharon Azulay, Tavi Halperin +3
Diffusion-based generative models have recently shown remarkable image and video editing capabilities. However, local video editing, particularly removal of small attributes like g…
DiffUHaul: A Training-Free Method for Object Dragging in Images
Omri Avrahami, Rinon Gal, Gal Chechik +4
Text-to-image diffusion models have proven effective for solving many image editing tasks. However, the seemingly straightforward task of seamlessly relocating objects within a sce…