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Handy: Towards a High Fidelity 3D Hand Shape and Appearance Model

CVPR 2023 · 3D vision › Human pose estimation · approach: Depth-based hand pose regression · 19 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use Handy rather than MANO for hand shape modelingHand reconstruction · Children's hands under age 12Per-vertex reconstruction error (mm): 0.77 -> 0.21Helped
Use the proposed high-resolution GAN texture model instead of HTMLHand texture reconstruction · Texture reconstructionLPIPS: 0.092 -> 0.010Helped
Train using the proposed synthetic dataset rather than Obman (ablation)Handy pose and shape reconstruction · FreiHandMPVPE: 9.9 -> 8.8Helped
Use a GAN texture model rather than a PCA texture model (ablation)Handy texture reconstruction · Texture reconstructionL1 (×10−2): 1.44 -> 0.47Helped
Increase shape components from 10 to 30 (ablation)Handy shape model · Children's hands under age 12Per-vertex reconstruction error (mm): 0.42 -> 0.21Helped
Use the higher-resolution Handy template rather than the MANO template (ablation)Handy shape model with 30 components · Children's hands under age 12Per-vertex reconstruction error (mm): 0.25 -> 0.21Helped
Increase shape components from 10 to 30 (ablation)Handy shape model · MANO dataset point cloudsR2S (mm): 0.16 -> 0.11Helped

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