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

2021-08-10CVPR 2022Code Available0· sign in to hype

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.

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
SkiPoseMetaPose (S1+S2)MPJPE53Unverified
SkiPoseMetaPose (S1+IR)MPJPE54Unverified

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