203 citations · 564 across the 9 of their papers we have counts for
8 papers · 1 filter
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…
Null-text Inversion for Editing Real Images using Guided Diffusion Models
Ron Mokady, Amir Hertz, Kfir Aberman +2
Recent text-guided diffusion models provide powerful image generation capabilities. Currently, a massive effort is given to enable the modification of these images using text only…
Deep Saliency Prior for Reducing Visual Distraction
Kfir Aberman, Junfeng He, Yossi Gandelsman +5
Using only a model that was trained to predict where people look at images, and no additional training data, we can produce a range of powerful editing effects for reducing distrac…
Neural Alignment for Face De-pixelization
Maayan Shuvi, Noa Fish, Kfir Aberman +2
We present a simple method to reconstruct a high-resolution video from a face-video, where the identity of a person is obscured by pixelization. This concealment method is popular…
Skeleton-Aware Networks for Deep Motion Retargeting
Kfir Aberman, Peizhuo Li, Dani Lischinski +3
We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Imp…
Learning Character-Agnostic Motion for Motion Retargeting in 2D
Kfir Aberman, Rundi Wu, Dani Lischinski +2
Analyzing human motion is a challenging task with a wide variety of applications in computer vision and in graphics. One such application, of particular importance in computer anim…