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Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals

Yuan, Shanxin and Garcia-Hernando, Guillermo and Stenger, Bjoern and Moon, Gyeongsik and Chang, Ju Yong and Lee, Kyoung Mu and Molchanov, Pavlo and Kautz, Jan and Honari, Sina and Ge, Liuhao and Yuan, Jungsong and Chen, Xinghao and Wang, Guijin and Yang, Fan and Akiyama, Kai and Wu, Yang and Madadi, Meysam and Escalera, Sergio and Li, Shile and Lee, Dongheui and Oikonomidis, Iason and Argyros, Antonis and Kim, Tae-Kyun (2018) Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals. In: 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018, pp. 2636-2645. IEEE. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), Salt-Lake City. doi: 10.1109/CVPR.2018.00279. ISBN 978-153866420-9. ISSN 1063-6919.

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Official URL: https://openaccess.thecvf.com/content_cvpr_2018/html/Yuan_Depth-Based_3D_Hand_CVPR_2018_paper.html

Abstract

In this paper, we strive to answer two questions: What is the current state of 3D hand pose estimation from depth images? And, what are the next challenges that need to be tackled? Following the successful Hands In the Million Challenge (HIM2017), we investigate the top 10 state-of-the-art methods on three tasks: single frame 3D pose estimation, 3D hand tracking, and hand pose estimation during object interaction. We analyze the performance of different CNN structures with regard to hand shape, joint visibility, view point and articulation distributions. Our findings include: (1) isolated 3D hand pose estimation achieves low mean errors (10 mm) in the view point range of [70, 120] degrees, but it is far from being solved for extreme view points; (2) 3D volumetric representations outperform 2D CNNs, better capturing the spatial structure of the depth data; (3) Discriminative methods still generalize poorly to unseen hand shapes; (4) While joint occlusions pose a challenge for most methods, explicit modeling of structure constraints can significantly narrow the gap between errors on visible and occluded joints.

Item URL in elib:https://elib.dlr.de/124905/
Document Type:Conference or Workshop Item (Speech)
Title:Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Yuan, ShanxinUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Garcia-Hernando, GuillermoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Stenger, BjoernUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Moon, GyeongsikUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Chang, Ju YongUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Lee, Kyoung MuUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Molchanov, PavloUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kautz, JanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Honari, SinaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Ge, LiuhaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Yuan, JungsongUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Chen, XinghaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Wang, GuijinUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Yang, FanUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Akiyama, KaiUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Wu, YangUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Madadi, MeysamUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Escalera, SergioUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Li, ShileTUMUNSPECIFIEDUNSPECIFIED
Lee, DongheuiUNSPECIFIEDhttps://orcid.org/0000-0003-1897-7664UNSPECIFIED
Oikonomidis, IasonUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Argyros, AntonisUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Kim, Tae-KyunUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:2018
Journal or Publication Title:31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1109/CVPR.2018.00279
Page Range:pp. 2636-2645
Publisher:IEEE
ISSN:1063-6919
ISBN:978-153866420-9
Status:Published
Keywords:Hand Pose Estimation, Challenges
Event Title:IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
Event Location:Salt-Lake City
Event Type:international Conference
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Space System Technology
DLR - Research area:Raumfahrt
DLR - Program:R SY - Space System Technology
DLR - Research theme (Project):R - Terrestrial Assistance Robotics (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013)
Deposited By: Ott, Dr. Christian
Deposited On:12 Dec 2018 08:06
Last Modified:14 Mar 2022 10:38

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