203 citations · 466 across the 4 of their papers we have counts for
6 papers
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…
Unpaired Motion Style Transfer from Video to Animation
Kfir Aberman, Yijia Weng, Dani Lischinski +2
Transferring the motion style from one animation clip to another, while preserving the motion content of the latter, has been a long-standing problem in character animation. Most e…
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…
Deep Video-Based Performance Cloning
Kfir Aberman, Mingyi Shi, Jing Liao +3
We present a new video-based performance cloning technique. After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor…
Neural Best-Buddies: Sparse Cross-Domain Correspondence
Kfir Aberman, Jing Liao, Mingyi Shi +3
Correspondence between images is a fundamental problem in computer vision, with a variety of graphics applications. This paper presents a novel method for sparse cross-domain corre…