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Research map / 3D vision / Face and shape

Learning Monocular 3D Reconstruction of Articulated Categories From Motion

CVPR 2021 · 3D vision › Face and shape · approach: Pixel-aligned implicit human and hand reconstruction · 16 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add motion-based training supervision (ablation)Horse reconstruction inference · HorsesmIoU: 0.519 -> 0.536Helped
Add motion-based training supervision (ablation)Horse reconstruction inference · HorsesPCK: 71.5 -> 74.7Helped
Add motion-based terms to mask refinement (ablation)Horse reconstruction · HorsesPCK: 79.5 -> 83.1Helped
Add motion-based terms to mask refinement (ablation)Horse reconstruction · HorsesmIoU: 0.691 -> 0.631Hurt
Add test-time refinementOurs, Horse TigDog with keypoints · Horse TigDog, with keypointsPCK: 74.7 -> 83.1Helped
Add test-time refinementOurs, Horse Pascal with keypoints · Horse Pascal, with keypointsPCK: 57.2 -> 69.5Helped
Add test-time refinementOurs, Tiger TigDog with keypoints · Tiger TigDog, with keypointsPCK: 51.9 -> 55.7Helped
Add test-time refinementOurs, mask-only Horse TigDog · Horse TigDog, mask-only trainingPCK: 42.5 -> 61.3Helped
Add test-time refinementOurs, mask-only Horse Pascal · Horse Pascal, mask-only trainingPCK: 31.6 -> 54.9Helped
Increase the number of deformation handles from 8 to 64 (ablation)Handle-based deformation · CUBmIoU: 0.64 -> 0.711Helped
Increase the number of deformation handles from 8 to 64 (ablation)Handle-based deformation · CUBPCK: 84.6 -> 91.5Helped
Use 8 deformation handles instead of 64 (ablation)Handle-based deformation · CUBPCK: 91.5 -> 84.6Hurt

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