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Research map / 3D vision / Human pose estimation

3D Hand Shape and Pose From Images in the Wild

CVPR 2019 · 3D vision › Human pose estimation · approach: Depth-based hand pose regression

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add 2D joint heat-maps to RGB input (ablation)Ours · Stereoaverage 3D joint distance (mm): 9.76 -> 10.18Hurt
Use 2D joint heat-maps instead of RGB input (ablation)Ours · Stereoaverage 3D joint distance (mm): 9.76 -> 10.46Hurt
Add 2D joint heat-maps to RGB input (ablation)Ours · Dexter+Objectaverage 3D joint distance (mm): 33.16 -> 25.53Helped
Use 2D joint heat-maps instead of RGB input (ablation)Ours · Dexter+Objectaverage 3D joint distance (mm): 33.16 -> 25.93Helped
Add 2D joint heat-maps to RGB input (ablation)Ours · EgoDexteraverage 3D joint distance (mm): 51.87 -> 45.58Helped
Use 2D joint heat-maps instead of RGB input (ablation)Ours · EgoDexteraverage 3D joint distance (mm): 51.87 -> 45.33Helped
Add 2D joint heat-maps to RGB input (ablation)Ours · Mpii+Nzsl testaverage re-projected 2D joint distance (px): 23.04 -> 18.95Helped
Use 2D joint heat-maps instead of RGB input (ablation)Ours · Mpii+Nzsl testaverage re-projected 2D joint distance (px): 23.04 -> 20.65Helped
Predict hand parameters from RGB and decode them with a fixed MANO hand model rather than fitting only 2D detectionsOurs RGB vs 2D fit · Stereoaverage 3D joint distance (mm): 23.21 -> 9.76Helped

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