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A2J-Transformer: Anchor-to-Joint Transformer Network for 3D Interacting Hand Pose Estimation From a Single RGB Image

CVPR 2023 · 3D vision › Human pose estimation · approach: Depth-based hand pose regression · 47 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add Transformer-based anchor refinement to A2J (ablation)A2J-Transformer · InterHand2.6MMPJPE (mm): 14.44 -> 9.63Helped
Use anchor-to-joint estimation instead of direct joint regression (ablation)A2J-Transformer · InterHand2.6MMPJPE (mm): 15.36 -> 9.63Helped
Predict adaptive 3D anchor weights instead of using uniform weights (ablation)A2J-Transformer · InterHand2.6MMPJPE (mm): 14.04 -> 9.63Helped
Use MSDAM instead of original attention (ablation)A2J-Transformer · InterHand2.6MMPJPE (mm): 10.69 -> 9.63Helped
Increase depth anchor values from 3 to 7 (ablation)A2J-Transformer · InterHand2.6MMPJPE (mm): 9.63 -> 9.50Helped
Increase depth anchor values from 3 to 5 (ablation)A2J-Transformer · InterHand2.6MMPJPE (mm): 9.63 -> 9.61Helped
Reduce depth anchor values from 3 to 1 (ablation)A2J-Transformer · InterHand2.6MMPJPE (mm): 9.63 -> 9.75Hurt
Reduce in-plane anchor values from 256 to 64 (ablation)A2J-Transformer · InterHand2.6MMPJPE (mm): 9.63 -> 12.28Hurt
Reduce in-plane anchor values from 256 to 16 (ablation)A2J-Transformer · InterHand2.6MMPJPE (mm): 9.63 -> 14.07Hurt
Reduce in-plane anchor values from 256 to 4 (ablation)A2J-Transformer · InterHand2.6MMPJPE (mm): 9.63 -> 15.48Hurt

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