This paper addresses the challenge of 3D full-body human pose estimation from
a monocular image sequence. Here, two cases are considered: (i) the image
locations of the human joints are provided and (ii) the image locations of
joints are unknown. In the former case, a novel approach is introduced that
integrates a sparsity-driven 3D geometric prior and temporal smoothness. In the
latter case, the former case is extended by treating the image locations of the
joints as latent variables. A deep fully convolutional network is trained to
predict the uncertainty maps of the 2D joint locations. The 3D pose estimates
are realized via an Expectation-Maximization algorithm over the entire
sequence, where it is shown that the 2D joint location uncertainties can be
conveniently marginalized out during inference. Empirical evaluation on the
Human3.6M dataset shows that the proposed approaches achieve greater 3D pose
estimation accuracy over state-of-the-art baselines. Further, the proposed
approach outperforms a publicly available 2D pose estimation baseline on the
challenging PennAction dataset.

本文通过提出一种新的方法，在人体姿势估计中实现了更好的 3D 姿势估计精度，通过使用单目图像序列并利用深度全卷积网络预测 2D 关节位置的不确定性图，并使用期望最大化算法进行 3D 姿势估计。