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PLIKS: A Pseudo-Linear Inverse Kinematic Solver for 3D Human Body Estimation

CVPR 2023 · 3D vision › Human pose estimation · approach: Human shape and pose regression · 45 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use PLIKS instead of the strongest listed baseline3DPW · 3DPW (14)PA-MPJPE: 43.0 -> 38.5Helped
Use PLIKS instead of the strongest listed baselineMPI-INF-3DHP · MPI-INF-3DHP (17)MPJPE: 93.9 -> 67.6Helped
Use PLIKS instead of the strongest listed baselineAGORA test · AGORA testMPJPE: 81.0 -> 71.5Helped
Add training with 3DPW and AGORA (ablation)PLIKS · 3DPW (14)PA-MPJPE: 42.8 -> 40.1Helped
Use the HRNet-W48 backbone instead of the standard backbone (ablation)PLIKS trained with 3DPW and AGORA · 3DPW (14)PA-MPJPE: 40.1 -> 38.5Helped
Add CamCalib focal-length estimation at evaluation (ablation)PLIKS · AGORA testNMJE: 83.0 -> 79.6Helped
Run the PLIKS module twice to refine rotations (ablation)PLIKS with GT correspondences · AGORAMPJPE: 8.4 -> 1.1Helped
Replace HRNet-W32 with ResNet-50 (ablation)ARE · 3DPWMPJPE: 83.6 -> 87.1Hurt
Use PLIKS instead of ARE alone (ablation)PLIKS · Human3.6MMPJPE: 53.6 -> 51.3Helped
Use estimated focal length f=√(W²+H²) instead of weak perspective (ablation)PLIKS · MPI-INF-3DHPMPJPE: 93.4 -> 83.2Helped
Use estimated rather than known camera intrinsics (ablation)PLIKS · MPI-INF-3DHPMPJPE: 76.9 -> 83.2Hurt
Use estimated rather than known camera intrinsics (ablation)PLIKS · 3DPWMPJPE: 81.8 -> 81.8No clear effect

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