mlsanityData to 7 Oct 2026

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

arXiv 2108.04869 ↗First posted 10 Aug 2021cs.CV cs.LGcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 48 of its 76 references that are in the map. First posted on arXiv 10 Aug 2021.

Builds on

48 of 76 references.

References · 48

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36 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 4; 2025: 1; 2026: 1.

12 papers.