Recurrent 3D Hand Pose Estimation Using Cascaded Pose-Guided 3D Alignments-Reference-Cited by-同舟云学术

Recurrent 3D Hand Pose Estimation Using Cascaded Pose-Guided 3D Alignments

Published:2023-01-01 Issue:1 Volume:45 Page:932-945
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Deng Xiaoming¹^ORCID,Zuo Dexin¹,Zhang Yinda²^ORCID,Cui Zhaopeng³^ORCID,Cheng Jian¹,Tan Ping⁴^ORCID,Chang Liang⁵^ORCID,Pollefeys Marc⁶,Fanello Sean²^ORCID,Wang Hongan¹

Affiliation:

1. Beijing Key Laboratory of Human Computer Interactions, Institute of Software, Chinese Academy of Sciences, Beijing, China

2. Google, Mountain View, CA, USA

3. State Key Lab of CAD&CG and the College of Computer Science and Technology, Zhejiang University, Hangzhou, China

4. School of Computing Science, Simon Fraser University, Burnaby, BC, Canada

5. School of Artificial Intelligence, Beijing Normal University, Beijing, China

6. Department of Computer Science, ETH Zurich, Zurich, Switzerland

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Beijing Natural Science Foundation

Distinguished Young Researcher Program, Institute of Software, Chinese Academy of Sciences

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

http://xplorestaging.ieee.org/ielx7/34/9970415/09736619.pdf?arnumber=9736619

Reference46 articles.

1. Context-Aware Deep Spatiotemporal Network for Hand Pose Estimation From Depth Images

2. One framework to register them all: Pointnet encoding for point cloud alignment;sarode,2019

3. Hand PointNet: 3D Hand Pose Estimation Using Point Sets

4. Point-To-Pose Voting Based Hand Pose Estimation Using Residual Permutation Equivariant Layer

5. PointNet++: Deep hierarchical feature learning on point sets in a metric space;qi;Proc Int Conf Neural Inf Process,2017

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