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MetaPose: Fast 3D Pose From Multiple Views Without 3D Supervision

CVPR 2022 · 3D vision › Human pose estimation · approach: Monocular 2D-to-3D pose lifting · 31 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add neural Stage 2 refinement to the Stage 1 initialization (ablation)MetaPose · Human3.6M, 4 camerasPMPJPE (mm): 74 -> 32Helped
Add neural Stage 2 refinement to the Stage 1 initialization (ablation)MetaPose · SkiPose-PTZ, 6 camerasPMPJPE (mm): 81 -> 42Helped
Replace neural refinement with iterative gradient-based refinement (ablation)MetaPose · Human3.6M, 4 camerasPMPJPE (mm): 32 -> 43Hurt
Train Stage 2 with reprojection supervision instead of the probabilistic bundle-adjustment objective (ablation)MetaPose · Human3.6M, 4 camerasPMPJPE (mm): 39 -> 32Helped
Use single-view pose estimates for initialization instead of random Gaussian noise (ablation)MetaPose · Human3.6M, 4 camerasPMPJPE (mm): 36 -> 32Helped
Replace permutation-equivariant blocks with concatenated-input MLPs (ablation)MetaPose · Human3.6M and SkiPose-PTZPMPJPE (mm): H36M, 4 cameras: 32 -> 30; SkiPose, 6 cameras: 42 -> 46Mixed
Add a personalized bone-length prior (ablation)MetaPose (S1+S2) · Human3.6M, 2 camerasPMPJPE (mm): 44 -> 37Helped
Add a teacher-student loss matching the iterative refiner (ablation)MetaPose (S1+S2) · Human3.6M and SkiPose-PTZPMPJPE (mm): 32 -> 38; 50 -> 72Hurt
Use a single Gaussian instead of a Gaussian mixture (ablation)MetaPose · Human3.6M, 2 camerasPMPJPE (mm): 44 -> 48Hurt

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