@article{SisLab3213,
           title = {A Spatial-temporal 3D Human Pose   Reconstruction Framework},
          author = {Xuan Thanh Nguyen and Thi Duyen Ngo and Thanh Ha Le},
       publisher = {KIPS},
            year = {2018},
         journal = {Journal of Information Processing Systems},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3213/},
        abstract = {3D human pose reconstruction from single-view camera is a difficult and challenging topic. Many approaches have been proposed, but almost focus on frame-by-frame independently while inter-frames are highly correlated in a pose sequence. In contrast, we introduce a novel spatial-temporal 3D 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. We quantitatively compare our framework with recent works on CMU motion capture dataset and Vietnamese traditional dance sequences. Our method outperforms others with 10 percent lower of Euclidean reconstruction error and robustness against Gaussian noise. Additionally, it is also important to mention that our reconstructed 3D pose sequences are smoother and more natural than others}
}