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Research map / 3D vision / Human pose estimation

Deep Multitask Architecture for Integrated 2D and 3D Human Sensing

CVPR 2017 · 3D vision › Human pose estimation · approach: Human shape and pose regression · 157 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add body-part maps B and joint maps J to the D-based reconstruction pipeline (ablation)DMHS_R on Human80K · Human80KAvg. MPJPE (mm): 77.56 -> 63.35Helped
Add body-part maps B to the J,D reconstruction input (ablation)DMHS_R on Human80K · Human80KAvg. MPJPE (mm): 72.00 -> 63.35Helped
Add image features D to the J-only reconstruction input (ablation)DMHS_R on Human80K · Human80KAvg. MPJPE (mm): 128.05 -> 72.00Helped
Add body-part maps B to J-only reconstruction (ablation)DMHS_R on Human80K · Human80KAvg. MPJPE (mm): 128.05 -> 118.68Hurt
Train body-part segmentation jointly on Human80K and LSP instead of Human80K alone (ablation)DMHS_B · Human80K testAvg. Acc. per class (fg + bg) (%): 68.56 -> 66.13Hurt
Fine-tune the Human80K segmentation model on LSP (ablation)DMHS_B · Human80K testAvg. Acc. per class (fg + bg) (%): 68.56 -> 45.61Hurt
Train body-part segmentation jointly on Human80K and LSP rather than Human80K alone (ablation)DMHS_B · LSPAvg. Acc. per class (fg + bg) (%): 38.77 -> 48.01Helped
Fine-tune the Human80K segmentation model on LSP (ablation)DMHS_B · LSPAvg. Acc. per class (fg + bg) (%): 38.77 -> 46.88Helped

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