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Computer Science > Computer Vision and Pattern Recognition

arXiv:2108.04869 (cs)
[Submitted on 10 Aug 2021 (v1), last revised 25 Nov 2021 (this version, v2)]

Title:MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision

Authors:Ben Usman, Andrea Tagliasacchi, Kate Saenko, Avneesh Sud
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Abstract:In the era of deep learning, human pose estimation from multiple cameras with unknown calibration has received little attention to date. We show how to train a neural model to perform this task with high precision and minimal latency overhead. The proposed model takes into account joint location uncertainty due to occlusion from multiple views, and requires only 2D keypoint data for training. Our method outperforms both classical bundle adjustment and weakly-supervised monocular 3D baselines on the well-established Human3.6M dataset, as well as the more challenging in-the-wild Ski-Pose PTZ dataset.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2108.04869 [cs.CV]
  (or arXiv:2108.04869v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2108.04869

Submission history

From: Ben Usman [view email]
[v1] Tue, 10 Aug 2021 18:39:56 UTC (15,983 KB)
[v2] Thu, 25 Nov 2021 23:16:01 UTC (14,631 KB)
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