Learning a 3D human pose distance metric from geometric pose descriptor

Chen, Cheng, Zhuang, Yueting, Nie, Feiping, Yang, Yi, Wu, Fei and Xiao, Jun (2011) Learning a 3D human pose distance metric from geometric pose descriptor. Ieee Transactions On Visualization and Computer Graphics, 17 11: 1676-1689. doi:10.1109/TVCG.2010.272

Author Chen, Cheng
Zhuang, Yueting
Nie, Feiping
Yang, Yi
Wu, Fei
Xiao, Jun
Title Learning a 3D human pose distance metric from geometric pose descriptor
Journal name Ieee Transactions On Visualization and Computer Graphics   Check publisher's open access policy
ISSN 1077-2626
Publication date 2011-11-01
Sub-type Article (original research)
DOI 10.1109/TVCG.2010.272
Open Access Status DOI
Volume 17
Issue 11
Start page 1676
End page 1689
Total pages 14
Place of publication Piscataway, NJ, United States
Publisher I E E E
Language eng
Subject 1712 Software
1711 Signal Processing
1707 Computer Vision and Pattern Recognition
1704 Computer Graphics and Computer-Aided Design
Abstract Estimating 3D pose similarity is a fundamental problem on 3D motion data. Most previous work calculates L2-like distance of joint orientations or coordinates, which does not sufficiently reflect the pose similarity of human perception. In this paper, we present a new pose distance metric. First, we propose a new rich pose feature set called Geometric Pose Descriptor (GPD). GPD is more effective in encoding pose similarity by utilizing features on geometric relations among body parts, as well as temporal information such as velocities and accelerations. Based on GPD, we propose a semisupervised distance metric learning algorithm called Regularized Distance Metric Learning with Sparse Representation (RDSR), which integrates information from both unsupervised data relationship and labels. We apply the proposed pose distance metric to applications of motion transition decision and content-based pose retrieval. Quantitative evaluations demonstrate that our method achieves better results with only a small amount of human labels, showing that the proposed pose distance metric is a promising building block for various 3D-motion related applications.
Keyword Human motion
Character animation
Pose features
Distance metric
Semisupervised learning
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 31 times in Thomson Reuters Web of Science Article | Citations
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