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Cross-Domain 3D Hand Pose Estimation With Dual Modalities

CVPR 2023 · 3D vision › Human pose estimation · approach: Depth-based hand pose regression · 13 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the dual-modality method instead of the strongest listed baselineCross-domain hand pose estimation · STBPCK@0.05: 0.655 -> 0.775Helped
Use the dual-modality method instead of the strongest listed baselineCross-domain hand pose estimation · FreiHANDPCK@0.05: 0.631 -> 0.658Helped
Use the dual-modality method instead of the strongest listed baselineCross-domain hand pose estimation · H3DPCK@0.05: 0.720 -> 0.749Helped
Use the dual-modality method instead of the strongest listed baselineCross-domain hand pose estimation · MVHandPCK@0.05: 0.640 -> 0.689Helped
Add depth-map supervision (ablation)RGB-only supervised baseline · STBEPE (mm): 19.66 -> 17.83Helped
Add attention-fused RGB supervision (ablation)RGB and depth supervised model · STBEPE (mm): 17.83 -> 16.92Helped
Add multi-modal contrastive learning (ablation)Attention-fused supervised model · STBEPE (mm): 16.92 -> 16.37Helped
Add pseudo-labeling (ablation)Pre-trained multi-modal model · STBEPE (mm): 16.37 -> 12.59Helped
Add self-distillation (ablation)Pseudo-labeled model · STBEPE (mm): 12.59 -> 11.99Helped
Add depth-map supervision (ablation)RGB-only supervised baseline · FreiHANDEPE (mm): 21.56 -> 20.14Helped
Add attention-fused RGB supervision (ablation)RGB and depth supervised model · FreiHANDEPE (mm): 20.14 -> 19.69Helped
Add multi-modal contrastive learning (ablation)Attention-fused supervised model · FreiHANDEPE (mm): 19.69 -> 19.19Helped

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