activity
20192021
most citedLearning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos

40 citations · 66 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CV202140 cited

Learning Dynamical Human-Joint Affinity for 3D Pose Estimation in Videos

Junhao Zhang, Yali Wang, Zhipeng Zhou +3

Graph Convolution Network (GCN) has been successfully used for 3D human pose estimation in videos. However, it is often built on the fixed human-joint affinity, according to human…

cs.CV20212 cited

Digging into Uncertainty in Self-supervised Multi-view Stereo

Hongbin Xu, Zhipeng Zhou, Yali Wang +4

Self-supervised Multi-view stereo (MVS) with a pretext task of image reconstruction has achieved significant progress recently. However, previous methods are built upon intuitions,…

cs.CL2021

When to Fold'em: How to answer Unanswerable questions

Marshall Ho, Zhipeng Zhou, Judith He

We present 3 different question-answering models trained on the SQuAD2.0 dataset -- BIDAF, DocumentQA and ALBERT Retro-Reader -- demonstrating the improvement of language models in…

cs.CV20211 cited

Self-supervised Multi-view Stereo via Effective Co-Segmentation and Data-Augmentation

Hongbin Xu, Zhipeng Zhou, Yu Qiao +2

Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multi-view stereo (MVS). However, existing methods rely on the assumptio…

cs.CR20212 cited

Practical Two-party Privacy-preserving Neural Network Based on Secret Sharing

Zhengqiang Ge, Zhipeng Zhou, Dong Guo +1

Neural networks, with the capability to provide efficient predictive models, have been widely used in medical, financial, and other fields, bringing great convenience to our lives.…

cs.CV20211 cited

PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos

Tianyu Luan, Yali Wang, Junhao Zhang +3

The end-to-end Human Mesh Recovery (HMR) approach has been successfully used for 3D body reconstruction. However, most HMR-based frameworks reconstruct human body by directly learn…