  Over the past decade, there has been a growing interest in human pose
estimation. Although much work has been done on 2D pose estimation, 3D pose
estimation has still been relatively studied less. In this paper, we propose a
top-bottom based two-stage 3D estimation framework. GloabalNet and RefineNet in
our 2D pose estimation process enable us to find occluded or invisible 2D
joints while 2D-to-3D pose estimator composed of residual blocks is used to
lift 2D joints to 3D joints effectively. The proposed method achieves promising
results with mean per joint position error at 42.39 on the validation dataset
on `3D Human Pose Estimation within the ECCV 2018 PoseTrack Challenge.'
