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PoseFix: Model-Agnostic General Human Pose Refinement Network

CVPR 2019 · 3D vision › Human pose estimation · approach: Multi-person pose estimation · 194 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Train the refiner model-agnostically on realistically synthesized errors rather than fine-tuning it end-to-end with CPNCPN pose refinement on MS COCO validation · MS COCO validationAP: 70.1 (+0.4) -> 72.1 (+2.4)Helped
Replace CPN RefineNet with model-agnostic PoseFixCPN GlobalNet refinement · MS COCO validationAP: 69.1 (+1.8) -> 71.5 (+4.2)Helped
Apply PoseFix to SimpleSimple · PoseTrack 2018 validationTotal AP: 70.4 -> 75.0Helped
Use a coarse Gaussian-blob input rather than a fine input-pose representation (ablation)PoseFix refinement pipeline · MS COCO validationpose refinement accuracyHelped
Estimate a fine-form output rather than a coarse Gaussian-heatmap output (ablation)PoseFix refinement pipeline · MS COCO validationpose refinement accuracyHelped
Use coordinate-only supervision instead of the combined heatmap and coordinate losses (ablation)C2F PoseFix · MS COCO validationpose refinement accuracyHurt
Use heatmap-only supervision instead of the combined heatmap and coordinate losses (ablation)C2F PoseFix · MS COCO validationpose refinement accuracyHurt

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