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arXiv · 1808.09316

How Robust is 3D Human Pose Estimation to Occlusion?

István Sárándi·Timm Linder·Kai O. Arras·Bastian Leibe
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Abstract

Occlusion is commonplace in realistic human-robot shared environments, yet its effects are not considered in standard 3D human pose estimation benchmarks. This leaves the question open: how robust are state-of-the-art 3D pose estimation methods against partial occlusions? We study several types of synthetic occlusions over the Human3.6M dataset and find a method with state-of-the-art benchmark performance to be sensitive even to low amounts of occlusion. Addressing this issue is key to progress in applications such as collaborative and service robotics. We take a first step in this direction by improving occlusion-robustness through training data augmentation with synthetic occlusions. This also turns out to be an effective regularizer that is beneficial even for non-occluded test cases.

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István Sárándi, Timm Linder, Kai O. Arras, Bastian Leibe. 2018-08-28. How Robust is 3D Human Pose Estimation to Occlusion?. https://arxiv.org/abs/1808.09316

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