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Research map / 3D vision / Human pose estimation

Recurrent 3D Pose Sequence Machines

CVPR 2017 · 3D vision › Human pose estimation · approach: Monocular 2D-to-3D pose lifting · 121 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use recurrent multi-stage pose refinementRPSM · Human3.6M3D pose error (mm), Avg.: 93.15 -> 73.10Helped
use RPSM instead of the strongest listed baseline3D pose estimation · HumanEva-I Walking3D pose error (mm), Avg.: 36.6 -> 28.4Helped
use RPSM instead of the strongest listed baseline3D pose estimation · HumanEva-I Jogging3D pose error (mm), Avg.: 38.9 -> 33.2Helped
use RPSM instead of Tekin et al.3D pose estimation · HumanEva-I Boxing3D pose error (mm), Avg.: 56.6 -> 52.8Helped
add a second refinement stage (ablation)RPSM · Human3.6M3D pose error (mm), Avg.: 80.15 -> 75.55Helped
add a third refinement stage (ablation)RPSM · Human3.6M3D pose error (mm), Avg.: 75.55 -> 73.10Helped
add three-stage refinement while omitting temporal modeling (ablation)RPSM · Human3.6M3D pose error (mm), Avg.: 90.83 -> 81.12Helped
add temporal modeling with five-frame clips (ablation)single-stage RPSM · Human3.6M3D pose error (mm), Avg.: 90.83 -> 80.15Helped
use ten-frame rather than five-frame clips (ablation)single-stage RPSM · Human3.6M3D pose error (mm), Avg.: 80.15 -> 82.88Hurt
remove MPII pretraining of the 2D pose module (ablation)RPSM-3-stage · Human3.6M3D pose error (mm), Avg.: 73.10 -> 111.3Hurt
share all 2D pose module layers across stages (ablation)RPSM-3-stage · Human3.6M3D pose error (mm), Avg.: 73.10 -> 71.44Helped

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