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Research map / 3D vision / Face and shape

3D Human Mesh Regression With Dense Correspondence

CVPR 2020 · 3D vision › Face and shape · approach: Learned mesh and geometric representations · 103 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use DecoMR instead of SPINMesh-based 3D human estimation · Human3.6M testMPJPE-PA P2: 41.1 -> 39.3Helped
Use DecoMR instead of BodyNet3D human shape reconstruction · SURREAL testSurface Error: 73.6 -> 56.5Helped
Use transferred local features instead of global features only (ablation)DecoMR with SMPL UV map · Human3.6M testMPJPE-PA P2: 49.1 -> 44.8Helped
Add transferred local features to global features (ablation)DecoMR with SMPL UV map · Human3.6M testMPJPE-PA P2: 49.1 -> 42.6Helped
Transfer raw image pixels instead of local image features (ablation)DecoMR with SMPL UV map · Human3.6M testMPJPE-PA P2: 42.6 -> 44.7Hurt
Use the continuous UV map instead of SMPL UV map (ablation)DecoMR with global feature only · Human3.6M testMPJPE-PA P2: 49.1 -> 47.1Helped
Use the continuous UV map instead of SMPL UV map (ablation)DecoMR with local feature only · Human3.6M testMPJPE-PA P2: 44.8 -> 42.3Helped
Use the continuous UV map instead of SMPL UV map (ablation)DecoMR with global and local features · Human3.6M testMPJPE-PA P2: 42.6 -> 39.3Helped
Use the continuous UV map instead of SMPL UV map (ablation)DecoMR with global feature and transferred raw pixels · Human3.6M testMPJPE-PA P2: 44.7 -> 42.6Helped
Use the continuous UV map instead of SMPL UV map (ablation)UV-map representation · Original mesh and UV-map distance matrices2D correlation (S1): 0.2132 -> 0.7758Helped

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