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Weakly-Supervised Domain Adaptation via GAN and Mesh Model for Estimating 3D Hand Poses Interacting Objects

CVPR 2020 · 3D vision › Human pose estimation · approach: Depth-based hand pose regression · 97 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Adapt HOI images to hand-only images using GAN and mesh-renderer guidance, without HOI 3D pose labels3D hand pose estimation · Dexter-Object3D PCKHelped
Adapt HOI images to hand-only images using GAN and mesh-renderer guidance, without HOI 3D pose labels3D hand pose estimation · Ego-Dexter3D PCKHelped
Adapt HOI images to hand-only images using GAN and mesh-renderer guidance, without HOI 3D pose labels3D hand pose estimation · HO3D EXTRAPerror rate: 28.24 vs 24.74Mixed
Adapt HOI images to hand-only images using GAN and mesh-renderer guidance, without HOI 3D pose labels3D hand pose estimation · HO3D OBJECTerror rate: 25.93 vs 18.39Mixed
Use HO3D 3D skeleton annotations during trainingProposed method · HO3Derror rate: 28.24 -> 23.63; 25.93 -> 20.59Helped
Add test-time iterative refinement of the MANO parameter using 2D joint consistency (ablation)Proposed method · Dexter-Object and Ego-Dexteraverage hand pose estimation accuracy: 4.3%; 6.5%Helped
Add GAN-based image adaptation after initial mesh-rendered reconstruction (ablation)Proposed method · Dexter-Object and Ego-Dexter3D PCKHelped
Add mesh-renderer guidance to GAN image adaptation (ablation)Proposed method · HOI hand-pose estimationpose estimation accuracyHelped

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