203 citations · 484 across the 8 of their papers we have counts for
8 papers · 1 filter
ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation
Jinming Cao, Hanchao Leng, Dani Lischinski +3
RGB-D semantic segmentation has attracted increasing attention over the past few years. Existing methods mostly employ homogeneous convolution operators to consume the RGB and dept…
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…
Cross-Domain Cascaded Deep Feature Translation
Oren Katzir, Dani Lischinski, Daniel Cohen-Or
In recent years we have witnessed tremendous progress in unpaired image-to-image translation methods, propelled by the emergence of DNNs and adversarial training strategies. Howeve…
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…
DiDA: Disentangled Synthesis for Domain Adaptation
Jinming Cao, Oren Katzir, Peng Jiang +4
Unsupervised domain adaptation aims at learning a shared model for two related, but not identical, domains by leveraging supervision from a source domain to an unsupervised target…