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Exploiting temporal information for 3D pose estimation
[article]
2018
arXiv
pre-print
In this work, we address the problem of 3D human pose estimation from a sequence of 2D human poses. Although the recent success of deep networks has led many state-of-the-art methods for 3D pose estimation to train deep networks end-to-end to predict from images directly, the top-performing approaches have shown the effectiveness of dividing the task of 3D pose estimation into two steps: using a state-of-the-art 2D pose estimator to estimate the 2D pose from images and then mapping them into 3D
arXiv:1711.08585v3
fatcat:rgitnojfi5f4dlbbnrjmfwd3ry