@InProceedings{ShanxinYuan2018,
author="Shanxin Yuan
and Guillermo Garcia-Hernando
and Bjorn Stenger
and Gyeongsik Moon
and Ju Yong Chang
and Kyoung Mu Lee
and Pavlo Molchanov
and Jan Kautz
and Sina Honari
and Liuhao Ge
and Junsong Yuan
and Xinghao Chen
and Guijin Wang
and Fan Yang
and Kai Akiyama
and Yang Wu
and Qingfu Wan
and Meysam Madadi
and Sergio Escalera
and Shile Li
and Dongheui Lee
and Iason Oikonomidis
and Antonis Argyros
and Tae-Kyun Kim",
title="Depth-Based 3D Hand Pose Estimation: From Current Achievements to Future Goals",
booktitle="31st IEEE Conference on Computer Vision and Pattern Recognition",
year="2018",
pages="2636--2645",
optkeywords="Three-dimensional displays",
optkeywords="Task analysis",
optkeywords="Pose estimation",
optkeywords="Two dimensional displays",
optkeywords="Joints",
optkeywords="Training",
optkeywords="Solid modeling",
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.",
optnote="HUPBA; no proj",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3115), last updated on Mon, 24 Jan 2022 12:20:00 +0100",
doi="10.1109/CVPR.2018.00279",
file=":http://refbase.cvc.uab.es/files/YGS2018.pdf:PDF"
}