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3D Human Pose Estimation Using Spatio-Temporal Networks with Explicit Occlusion Training

AAAI 2020 · 3D vision › Human pose estimation · approach: Monocular 2D-to-3D pose lifting · 132 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add heat map embedding (ablation)3D pose TCN · Human3.6MMPJPE (Protocol #1): 51.7 -> 51.0Helped
add multi-stride TCN (ablation)3D pose TCN · Human3.6MMPJPE (Protocol #1): 51.0 -> 48.6Helped
add multi-view loss (ablation)3D pose TCN · Human3.6MMPJPE (Protocol #1): 48.6 -> 47.3Helped
add spatial KCS (ablation)3D pose TCN · Human3.6MMPJPE (Protocol #1): 47.3 -> 44.9Helped
add temporal KCS (ablation)3D pose TCN with spatial KCS · Human3.6MMPJPE (Protocol #1): 44.9 -> 41.2Helped
add semi-supervised 2D data (ablation)3D pose TCN with KCS · Human3.6MMPJPE (Protocol #1): 41.2 -> 40.1Helped
increase temporal strides from 1 to 1,2,3,5,7 (ablation)3D pose TCN · Human3.6MMPJPE (Protocol #1): 45.4 -> 41.2Helped
increase temporal length from 8 to 64 (ablation)3D pose TCN · Human3.6MMPJPE (Protocol #1): 50.2 -> 42.6Helped
increase embedding dimension from 256 to 512 (ablation)heat map embedding · Human3.6MMPJPE (Protocol #1): 43.1 -> 42.6Helped
increase embedding dimension from 512 to 1024 (ablation)heat map embedding · Human3.6MMPJPE (Protocol #1): 42.6 -> 42.9No clear effect
increase temporal interval for TKCS from 1 to 3 (ablation)spatio-temporal discriminator · Human3.6MMPJPE (Protocol #1): 42.6 -> 43.1Hurt
increase temporal interval for TKCS from 1 to 5 (ablation)spatio-temporal discriminator · Human3.6MMPJPE (Protocol #1): 42.6 -> 44.0Hurt

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