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Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation

CVPR 2020 · 3D vision › Human pose estimation · approach: Monocular 2D-to-3D pose lifting · 67 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add multiview-consistent metric learning to pose-regression trainingMCSS on Human3.6M with S1 supervision · Human3.6M Protocol 2, S1 supervisionN-MPJPE: 149.28 -> 111.94Helped
Regress canonical rather than global-coordinate pose (ablation)MCSS on Human3.6M with S1 supervision · Human3.6M Protocol 2, S1 supervisionN-MPJPE: 157.30 -> 111.94Helped
Use ResNet-34 instead of the ResNet-18 backbone (ablation)MCSS on Human3.6M with S1 supervision · Human3.6M Protocol 2, S1 supervisionN-MPJPE: 111.94 -> 115.85Hurt
Fine-tune the Human3.6M-trained embedding with multiview metric loss on MPI-INF-3DHPMCSS cross-view pose retrieval · MPI-INF-3DHPMean-PA-MPJPE@10: 119.6 -> 62.46Helped
Use the smaller ResNet-18 feature extractor rather than ResNet-50MCSS inference · NVIDIA 1080Ti inferenceinference time: 75.3ms -> 24.8msHelped

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