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Research map / 3D vision / Human pose estimation

Body Meshes as Points

CVPR 2021 · 3D vision › Human pose estimation · approach: Human mesh recovery from images · 56 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use a single-stage spatial-depth point representation for localization and mesh recoveryBMP · PanopticMPJPE: 143.2 -> 135.4Helped
use a single-stage spatial-depth point representation for localization and mesh recoveryBMP · MuPoTS-3D3DPCK (All): 69.12 -> 73.83Helped
use a single-stage spatial-depth point representation for localization and mesh recoveryBMP · 3DPWPCK: 25.8 -> 32.1; 33.9Mixed
replace the 2D spatial representation with the spatial-depth representation and FPN depth levels (ablation)BMP · PanopticMPJPE: 145.6 -> 135.4Helped
replace the 2D spatial representation with the spatial-depth representation and FPN depth levels (ablation)BMP · 3DPWMPJPE: 110.8 -> 104.1Helped
replace the 2D spatial representation with the spatial-depth representation and FPN depth levels (ablation)BMP · MuPoTS-3D3DPCK: 69.34 -> 73.83Helped
add full pair-wise ordinal depth loss (ablation)BMP · MuPoTS-3DRelative depth ordering accuracy: 91.42% -> 94.50%Helped
add keypoint-aware occlusion augmentation (ablation)BMP · PanopticMPJPE: 148.9 -> 135.4Helped
add keypoint-aware occlusion augmentation (ablation)BMP · 3DPWMPJPE: 125.9 -> 104.1Helped
add keypoint-aware occlusion augmentation (ablation)BMP · MuPoTS-3D3DPCK: 71.71 -> 73.83Helped
replace keypoint-aware occlusion augmentation with random synthetic occlusion (ablation)BMP · MuPoTS-3D3DPCK: 71.71 -> 70.78Hurt

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