activity
20172021
most citedSkeleton-Aware Networks for Deep Motion Retargeting

203 citations · 484 across the 8 of their papers we have counts for

collaborators

12 papers

cs.CV20211 cited

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…

eess.IV20201 cited

Evaluation and Comparison of Edge-Preserving Filters

Sarah Gingichashvili, Dani Lischinski

Edge-preserving filters play an essential role in some of the most basic tasks of computational photography, such as abstraction, tonemapping, detail enhancement and texture remova…

cs.GR20202 cited

Palettailor: Discriminable Colorization for Categorical Data

Kecheng Lu, Mi Feng, Xin Chen +5

We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, and bar charts. While other methods…

cs.GR2020174 cited

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…

cs.CV2020203 cited

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…

cs.CV201910 cited

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…