output
20192023
most citedSkeleton-Aware Networks for Deep Motion Retargeting

203 citations

6 papers

cs.CV20213 cited

Unsupervised Co-part Segmentation through Assembly

Qingzhe Gao, Bin Wang, Libin Liu +1

Co-part segmentation is an important problem in computer vision for its rich applications. We propose an unsupervised learning approach for co-part segmentation from images. For th…

physics.flu-dyn202119 cited

Solid-Fluid Interaction with Surface-Tension-Dominant Contact

Liangwang Ruan, Jinyuan Liu, Bo Zhu +3

We propose a novel three-way coupling method to model the contact interaction between solid and fluid driven by strong surface tension. At the heart of our physical model is a thin…

cs.CV2020141 cited

Unified Quality Assessment of In-the-Wild Videos with Mixed Datasets Training

Dingquan Li, Tingting Jiang, Ming Jiang

Video quality assessment (VQA) is an important problem in computer vision. The videos in computer vision applications are usually captured in the wild. We focus on automatically as…

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.HC2019

Symmetrical Reality: Toward a Unified Framework for Physical and Virtual Reality

Zhenliang Zhang, Cong Wang, Dongdong Weng +2

In this paper, we review the background of physical reality, virtual reality, and some traditional mixed forms of them. Based on the current knowledge, we propose a new unified con…