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Differentiable Dynamics for Articulated 3D Human Motion Reconstruction

CVPR 2022 · 3D vision › Human pose estimation · approach: Physics and sensor-based motion capture · 37 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add differentiable physics-based reconstructionKinematic estimates · Human3.6MMPJPE-G: 145.3 -> 139.1Helped
add differentiable physics-based reconstructionKinematic estimates · Human3.6MFoot skate (%): 47.5 -> 7.4Helped
add differentiable physics-based reconstructionKinematic estimates · AISTMPJPE-G: 155.7 -> 150.2Helped
add differentiable physics-based reconstructionKinematic estimates · AISTMPJPE-2d: 10.4 -> 12.1Hurt
add differentiable physics-based reconstructionKinematic estimates · AISTFoot skate (%): 50.9 -> 19.6Helped
use Basin-Hopping with BFGS instead of local BFGS aloneDiffPhy optimization · Human3.6M validationMPJPE-G: 160.1 -> 144.9Helped
use BFGS instead of CMA-ESDiffPhy optimization · Human3.6M validationMPJPE-G: 206.7 -> 160.1Helped
add root position loss (ablation)DiffPhy · Human3.6M ablation subsetMPJPE-G: 165.7 -> 144.9Helped
add 2d keypoint loss (ablation)DiffPhy · Human3.6M ablation subsetMPJPE-2d: 17.1 -> 12.6Helped
add joint angle loss (ablation)DiffPhy · Human3.6M ablation subsetMPJPE-G: 156.8 -> 144.9Helped
add root position and joint angle losses (ablation)DiffPhy · Human3.6M ablation subsetMPJPE-G: 216.6 -> 144.9Helped
add anatomical joint limits (ablation)DiffPhy · Human3.6M ablation subsetMPJPE-PA: 62.2 -> 61.1Helped

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