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Foreground-Background Segmentation on LSP (test)

92.79AccuracyPyMAF
86.352488.023789.69591.3663Dec 18, 2017Jul 5, 2018Jan 21, 2019Aug 9, 2019Feb 24, 2020Sep 11, 2020Mar 30, 2021

Evaluation results

MethodLinks
PyMAF2021.0392.790.89
BodynetApproach type=Optimization-based2019.0592.750.84
BodyNet2021.0392.750.84
PyMAF w/o ASAuxiliary Supervision=false2021.0392.430.88
HKMR2021.0392.230.88
SMPLify oracleground truth segmentation=true, Run Time=-2017.1292.170.88
SMPLify oracleApproach type=Optimization-based, Oracle=true2019.0592.170.88
SMPLify on [31]Approach type=Optimization-based, Reference initialization=Pavlakos et al. [31]2019.0592.170.88
SMPLify oracleoracle=true2019.1092.170.88
SMPLify on [35]initialization=Pavlakos et al. [35]2019.1092.170.88
SMPLifyoracle=true2020.0692.170.88
SMPLifyinitialization=Pavlakos et al. [26]2020.0692.170.88
SMPLify oracleground-truth keypoints=true2021.0392.170.88
SMPLify on [44]detector=Pavlakos et al. [44]2021.0392.170.88
DecoMR2020.0692.10.88
DecoMR2021.0392.10.88
SMPLifyRun Time=~1 min2017.1291.890.88
SMPLifyApproach type=Optimization-based2019.0591.890.88
SMPLify2019.1091.890.88
SMPLify2020.0691.890.88
SMPLify2021.0391.890.88
SPIN2020.0691.830.87
SPIN2021.0391.830.87
TexturePose2019.1091.820.87
TexturePose2021.0391.820.87
HMRpaired 3D supervision=true, Run Time=0.04 sec2017.1291.670.87
HMRApproach type=Regression-based2019.0591.670.87
HMR2019.1091.670.87
HMR2020.0691.670.87
HMR2021.0391.670.87
Baselinedescription=same network architecture with HMR and 6D rotation representation2021.0391.670.87
Ours (CMR)Approach type=Regression-based2019.0591.460.87
CMR2020.0691.460.87
CMR2021.0391.460.87
HMR unpairedpaired 3D supervision=false, Run Time=0.04 sec2017.1291.30.86
Decision ForestsRun Time=0.13 sec2017.1286.60.8