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In this paper we present our winning entry at the 2018 ECCV PoseTrackChallenge on 3D human pose estimation. Using a fully-convolutional backbonearchitecture, we obtain volumetric heatmaps per body joint, which we convert tocoordinates using soft-argmax. Absolute person center depth is estimated by a1D heatmap prediction head. The coordinates are back-projected to 3D cameraspace, where we minimize the L1 loss. Key to our good results is the trainingdata augmentation with randomly placed occluders from the Pascal VOC dataset.In addition to reaching first place in the Challenge, our method also surpassesthe state-of-the-art on the full Human3.6M benchmark among methods that use noadditional pose datasets in training. Code for applying synthetic occlusions isavailabe at https://github.com/isarandi/synthetic-occlusion.

Synthetic Occlusion Augmentation with Volumetric Heatmaps for the 2018 ECCV PoseTrack Challenge on 3D Human Pose Estimation