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Research map / 3D vision / Human pose estimation

HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation From a Single Depth Map

CVPR 2020 · 3D vision › Human pose estimation · approach: Depth-based hand pose regression · 81 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add V2V-SynNet and S2V-SynNet weak-supervision synthesizers (ablation)HandVoxNet · SynHand5M test3D vertex error (mm): 2.92 -> 2.67Helped
add 3D joint heatmaps to V2S-Net input (ablation)V2S-Net with voxelized depth input · SynHand5M3D surface vertex error (mm): 8.78 -> 3.36Helped
add voxelized depth input to V2S-Net (ablation)V2S-Net with heatmaps · SynHand5M3D surface vertex error (mm): 3.54 -> 3.36Helped
add 3D joint heatmaps to V2V-ShapeNet input (ablation)V2V-ShapeNet with voxelized depth input · SynHand5M3D voxelized shape error: 0.016 -> 0.005Helped
add voxelized depth input to V2V-ShapeNet (ablation)V2V-ShapeNet with heatmaps · SynHand5M3D voxelized shape error: 0.007 -> 0.005Helped
register the estimated surface to the voxelized shape with DispVoxNet (ablation)HandVoxNet · SynHand5M3D surface vertex error (mm): 3.36mm to 2.67mmHelped
apply 3D augmentation to voxelized depth maps (ablation)V2V-PoseNet · NYU3D joint error (mm): 9.22 -> 8.72Helped
apply 3D augmentation to voxelized depth maps (ablation)V2V-PoseNet · BigHand2.2M3D joint error (mm): 9.95 -> 9.27Helped
train pose estimation jointly with the shape-regression pipelineHandVoxNet on SynHand5M · SynHand5M test3D joint error (mm)Helped

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