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. Our code will be publicly available (https://github.com/vegesm/wdspose).
CITATION STYLE
Véges, M., & Lőrincz, A. (2020). Multi-person Absolute 3D Human Pose Estimation with Weak Depth Supervision. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12396 LNCS, pp. 258–270). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-61609-0_21
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