A Spatial-temporal 3D Human Pose Reconstruction Framework


Xuan Thanh Nguyen, Thi Duyen Ngo, Thanh Ha Le, Journal of Information Processing Systems
Vol. 15, No. 2, pp. 399-409, Apr. 2019
10.3745/JIPS.02.0110
Keywords: 3D Human Pose, Reconstruction, Spatial-Temporal Model
Fulltext:

Abstract

Three-dimensional (3D) human pose reconstruction from single-view image is a difficult and challenging topic. Existing approaches mostly process frame-by-frame independently while inter-frames are highly correlated in a sequence. In contrast, we introduce a novel spatial-temporal 3D human pose reconstruction framework that leverages both intra and inter-frame relationships in consecutive 2D pose sequences. Orthogonal matching pursuit (OMP) algorithm, pre-trained pose-angle limits and temporal models have been implemented. Several quantitative comparisons between our proposed framework and recent works have been studied on CMU motion capture dataset and Vietnamese traditional dance sequences. Our framework outperforms others by 10% lower of Euclidean reconstruction error and more robust against Gaussian noise. Additionally, it is also important to mention that our reconstructed 3D pose sequences are more natural and smoother than others.


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Cite this article
[APA Style]
Xuan Thanh Nguyen, Thi Duyen Ngo, & Thanh Ha Le (2019). A Spatial-temporal 3D Human Pose Reconstruction Framework. Journal of Information Processing Systems, 15(2), 399-409. DOI: 10.3745/JIPS.02.0110.

[IEEE Style]
X. T. Nguyen, T. D. Ngo and T. H. Le, "A Spatial-temporal 3D Human Pose Reconstruction Framework," Journal of Information Processing Systems, vol. 15, no. 2, pp. 399-409, 2019. DOI: 10.3745/JIPS.02.0110.

[ACM Style]
Xuan Thanh Nguyen, Thi Duyen Ngo, and Thanh Ha Le. 2019. A Spatial-temporal 3D Human Pose Reconstruction Framework. Journal of Information Processing Systems, 15, 2, (2019), 399-409. DOI: 10.3745/JIPS.02.0110.