  In this paper we present our winning entry at the 2018 ECCV PoseTrack
Challenge on 3D human pose estimation. Using a fully-convolutional backbone
architecture, we obtain volumetric heatmaps per body joint, which we convert to
coordinates using soft-argmax. Absolute person center depth is estimated by a
1D heatmap prediction head. The coordinates are back-projected to 3D camera
space, where we minimize the L1 loss. Key to our good results is the training
data augmentation with randomly placed occluders from the Pascal VOC dataset.
In addition to reaching first place in the Challenge, our method also surpasses
the state-of-the-art on the full Human3.6M benchmark among methods that use no
additional pose datasets in training. Code for applying synthetic occlusions is
availabe at https://github.com/isarandi/synthetic-occlusion.
