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Research map / 3D vision / Human pose estimation

Capturing Humans in Motion: Temporal-Attentive 3D Human Pose and Shape Estimation From Monocular Video

CVPR 2022 · 3D vision › Human pose estimation · approach: Human shape and pose regression · 126 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use MPS-Net instead of TCMR3DPW · 3DPWMPJPE: 86.5 -> 84.3Helped
Use MPS-Net instead of TCMRMPI-INF-3DHP · MPI-INF-3DHPPA-MPJPE: 63.5 -> 62.8Helped
Use MPS-Net instead of TCMRHuman3.6M · Human3.6MMPJPE: 73.6 -> 69.4Helped
Use MPS-Net instead of TCMR3DPW · 3DPWACC-ERR: 6.8 -> 7.4Hurt
Use MPS-Net instead of TCMRMPI-INF-3DHP · MPI-INF-3DHPACC-ERR: 8.5 -> 9.6Hurt
Add NSSM-guided MoCA instead of using only a non-local block (ablation)MPS-Net, without HAFI · 3DPWMPJPE: 87.6 -> 86.7Helped
Add HAFI instead of leaving the model with MoCA only (ablation)MPS-Net · 3DPWMPJPE: 86.7 -> 84.3Helped
Use HAFI instead of temporal feature integration (ablation)MPS-Net with MoCA · 3DPWACC-ERR: 10.5 -> 7.4Helped
Use 3 frames per HAFI group rather than 2 (ablation)MPS-Net · 3DPWMPJPE: 85.4 -> 84.3Helped
Use 3 frames per HAFI group rather than 4 (ablation)MPS-Net · 3DPWMPJPE: 85.9 -> 84.3Helped
Add HAFI to a MoCA-free model (ablation)MPS-Net, HAFI only · 3DPWMPJPE: 87.6; 86.7Mixed
Use MoCA and HAFI together rather than only HAFI (ablation)MPS-Net · 3DPWMPJPE: 87.6 -> 84.3Helped

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