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Issue No.11 - November (2011 vol.17)

pp: 1676-1689

Cheng Chen , Idiap Research Institute, Martigny

Feiping Nie , University of Texas at Arlington, Arlington

Yi Yang , ITEE, The University of Queensland, Brisbane

Fei Wu , Zhejiang University, Hangzhou

Jun Xiao , Zhejiang University, Hangzhou

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.272

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.

INDEX TERMS

Human motion, character animation, pose features, distance metric, semisupervised learning.

CITATION

Cheng Chen, Feiping Nie, Yi Yang, Fei Wu, Jun Xiao, "Learning a 3D Human Pose Distance Metric from Geometric Pose Descriptor",

*IEEE Transactions on Visualization & Computer Graphics*, vol.17, no. 11, pp. 1676-1689, November 2011, doi:10.1109/TVCG.2010.272REFERENCES

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