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Research map / 3D vision / Human pose estimation

3D Human Pose Estimation = 2D Pose Estimation + Matching

CVPR 2017 · 3D vision › Human pose estimation · approach: Monocular 2D-to-3D pose lifting · 604 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
predict 2D pose, then match and warp a 3D exemplar3D human pose estimation · Human3.6M Protocol 1MPJPE: 82.72 vs 108.3; 88.1Helped
use 2D pose estimation plus matching without temporal input3D human pose estimation · Human3.6M Protocol 2MPJPE: 114.18 vs 113.01; 124.97Mixed
add exemplar warping (ablation)nearest-neighbor 3D pose matching · Human3.6M Protocol 1, predicted 2DMPJPE: 85.52 -> 82.72Helped
add exemplar warping (ablation)nearest-neighbor 3D pose matching · Human3.6M Protocol 1, ground-truth 2DMPJPE: 70.93 -> 57.50Helped
use the optimal 3D exemplar and warp it (ablation)3D exemplar matching · Human3.6M Protocol 1, optimal exemplar matchMPJPE: 60.11 -> 37.32Helped
increase the library from 18k to 180k pairs (ablation)3D exemplar matching · Human3.6M Protocol 1MPJPE: 85.94 -> 82.37Helped
increase the library from 180k to 1,800k pairs (ablation)3D exemplar matching · Human3.6M Protocol 1MPJPE: 82.37 -> 82.72Hurt
add exemplar warping (ablation)Human3.6M-trained exemplar matching · HumanEva-I, ground-truth 2DMPJPE: 92.17 -> 68.29Helped
increase the shortlist from k=1 to k=10 and search over virtual cameras (ablation)warped exemplar matching · Human3.6M Protocol 2, ground-truth 2DMPJPE: 51.06 -> 49.55Helped

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