Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision

• URL: http://arxiv.org/abs/2004.03989v1
• Date: Wed, 8 Apr 2020 13:29:22 GMT
• Title: Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision
• Authors: Marton Veges, Andras Lorincz
• Abstract summary: We introduce a network that can be trained with additional RGB-D images in a weakly supervised fashion. Our algorithm is a monocular, multi-person, absolute pose estimator. We evaluate the algorithm on several benchmarks, showing a consistent improvement in error rates.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In 3D human pose estimation one of the biggest problems is the lack of large, diverse datasets. This is especially true for multi-person 3D pose estimation, where, to our knowledge, there are only machine generated annotations available for training. To mitigate this issue, we introduce a network that can be trained with additional RGB-D images in a weakly supervised fashion. Due to the existence of cheap sensors, videos with depth maps are widely available, and our method can exploit a large, unannotated dataset. Our algorithm is a monocular, multi-person, absolute pose estimator. We evaluate the algorithm on several benchmarks, showing a consistent improvement in error rates. Also, our model achieves state-of-the-art results on the MuPoTS-3D dataset by a considerable margin.

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