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3064 Journal of Software 软件学报 Vol.32, No.10, October 2021

[21] Ge LH, Cai YJ, Weng GW, Yuan JS. Hand pointnet: 3D hand pose estimation using point sets. In: Proc. of the IEEE Conf. on
Computer Vision and Pattern Recognition. 2018. 84178426.
[22] Moon GS, Chang YJ, Lee KM. V2v-Posenet: Voxel-to-voxel prediction network for accurate 3D hand and human pose estimation
from a single depth map. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2018. 50805088.
[23] Rad M, Oberweger M, Lepetit V. Feature mapping for learing fast and accurate 3D pose inference from synthetic images. In: Proc.
of the IEEE Conf. on Computer Vision and Pattern Recognition. 2018. 46634672.
[24] Poier G, Schinagl D, Bischof H. Learning pose specific representations by predicting different views. In: Proc. of the IEEE Conf.
on Computer Vision and Pattern Recognition. 2018. 6069.
[25] Baek SR, Kim KI, Kim TK. Augmented skeleton space transfer for depth-based hand pose estimation. In: Proc. of the IEEE Conf.
on Computer Vision and Pattern Recognition. 2018. 83308339.
[26] Dibra E, Wolf T, Oztireli C, Gross M. How to refine 3D hand pose estimation from unlabelled depth data? In: Proc. of the 2017
Int’l Conf. on 3D Vision (3DV). IEEE, 2017. 135144.
[27] Wan CD, Probst T, Van Gool L, Yao A. Dense 3D regression for hand pose estimation. In: Proc. of the IEEE Conf. on Computer
Vision and Pattern Recognition. 2018. 51475156.
[28] Ye Q, Yuan S, Kim TK. Spatial attention deep net with partial PSO for hierarchical hyrid hand pose estimation. In: Proc. of the
European Conf. on Computer Vision. Springer-Verlag, 2016. 346361.
[29] Malik J, Elhayek A, Nummari F, Varanasi K, Tamaddon K, Heloir A, Stricker D. DeepHPS: End-to-end estimation of 3D hand
pose and shape by learning from synthetic depth. In: Proc. of the 2018 Int’l Conf. on 3D Vision (3DV). Verona, 2018. 110119.
[doi: 10.1109/3DV.2018.00023]
[30] Zimmermann C, Brox T. Learning to estimate 3D hand pose from single RGB images. In: Proc. of the IEEE Conf. on Computer
Vision and Pattern Recognition. 2017.
[31] Simon T, Joo H, Matthews I, Sheikh Y. Hand keypoint detection in single images using multiview bootstrapping. In: Proc. of the
IEEE Conf. on Computer Vision and Pattern Recognition. 2017. 11451153.
[32] Mueller F, Bernard F, Sotnychenko O, Mehta D, Sridhar S, Casas D, Theobalt C. GANerated hands for real-time 3D hand tracking
from monocular RGB. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2018. 4959.
[33] Spurr A, Song J, Park S, Hilliges O. Cross-modal deel variational hand pose estimation. In: Proc. of the IEEE Conf. on Computer
Vision and Pattern Recognition. 2018. 8998.
[34] Lien J, Gillian N, Karagozler ME, Amyhood P, Schwesig C, Olson E, Raja H, Poupyrev I. Soli: Ubiquitous gesture sensing with
millimeter wave radar. ACM Trans. on Graphics, 2016,35(4):119.
[35] Nymoen K, Haugen MR, Jensenius AR. MuMYO—Evaluating and exploring the MYO armband for musical interaction. In: Proc.
of the Int’l Conf. on New Interfaces for Musical Expression. The School of Music and the Center for Computation and Technology
(CCT), Louisiana State University, 2015.
[36] Han SC, Liu BB, Wang R, Ye YT, Twigg CD, Chen K. Online optical marker-based hand tracking with deep labels. ACM Trans.
on Graphics, 2018,34(7):166.
[37] Ng CW, Ranganath S. Real-time gesture recognition system and application. Image and Vision Computing, 2002,20:9931007.
[38] Cheng H, Yang L, Liu ZC. A survey on 3D hand gesture recognition. IEEE Trans. on Circuits and Systems for Video Technology,
2015,26:1.
[39] Elmezain M, Al-Hamadi A, Appenrodt J, Michaelis B. A hidden markov model-based continuous gesture recognition system for
hand motion trajectory. In: Proc. of the 19th Int’l Conf. on Pattern Recognition. 2008. 14.
[40] Keskin C, Cemgil AT, Akarun L. DTW based clustering to improve hand gesture recognition. In: Proc. of the Human Behavior
Understanding. 2011. 7281.
[41] Arici T, Celebi S, Aydin AS, Temiz TT, Robust gesture recognition using feature pre-processing and weighted dynamic time
warping. Multimedia Tools Application, 2014,72(3):30453062.
[42] Reyes M, Dominguez G, Escalera S. Feature weighting in dynamic time warping for gesture recognition in depth data. In: Proc. of
the IEEE Int’l Conf. on Computer Vision Workshops. 2011. 11821188.