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3D Human Mesh Estimation on 3DPW (test)

44.1PA-MPJPERemoCap
40.92862.33983.75105.161Apr 15, 2021Oct 20, 2021Apr 27, 2022Nov 2, 2022May 9, 2023Nov 14, 2023May 21, 2024

Evaluation results

MethodLinks
RemoCapApproach category=Video-based Model-Free, Post-processing=false2024.0544.172.781.9———
METROTraining sets=H36M+UP3D+MuCo+3DPW+MPII+[COCO]Ours2023.0445.8——17.917.231.3
RemoCapApproach category=Video-based Model-Free, Post-processing=true2024.0545.974.5————
I2L-MeshNetTraining sets=H36M+MuCo+[COCO]Ours2023.0447.1——17.118.532.6
3DCrowdNetTraining sets=H36M+MuCo+CrowdPose+[COCO+MPII]Ours2023.0447.2——16.817.733.5
PARETraining sets=[COCO]Ours2023.0447.3——17.518.232.9
METROTraining sets=H36M+UP3D+MuCo+3DPW+MPII+[COCO]SMPLify-X2023.0447.9——18.818.532.4
Pose2PoseTraining sets=H36M+[COCO+MPII]Ours2023.0449.6——18.418.834.7
GLOTApproach category=Video-based2024.0550.680.796.3———
PyMAFTraining sets=H36M+MI+[COCO+MPII+LSPET]Ours2023.0450.9——1820.835.1
CycleAdaptSource dataset=SURREAL [41], 2D pose estimator=OpenPose [3]2023.0851.184.499.9———
3DCrowdNetTrain set=No 3DPW train set2021.0451.581.798.3———
3DCrowdNetTraining sets=H36M+MuCo+CrowdPose+[COCO+MPII]NeuralAnnot2023.0451.5——17.618.236.3
SPINTraining sets=H36M+MI+[COCO+MPII+LSPET]Ours2023.0451.6——1920.735.4
MPS-NetApproach category=Video-based2024.0552.184.399.7———
TCMRApproach category=Video-based2024.0552.786.5102.9———
Pose2PoseTraining sets=H36M+[COCO+MPII]Neural Annot2023.0454.4——19.120.339
MEVAApproach category=Video-based2024.0554.786.9————
Fang et al.Train set=No 3DPW train set2021.0454.885.1————
ROMPTrain set=No 3DPW train set2021.0454.991.3108.3———
CycleAdaptSource Dataset=SURREAL, Adaptation Scenario=Online, 2D Keypoints Source=OpenPose2023.0855.190106.8———
CycleAdaptSource Dataset=Human3.6M, Adaptation Scenario=Online, 2D Keypoint Detector=OpenPose2023.0855.290.3107———
TUCHTrain set=No 3DPW train set2021.0455.584.9————
Song et al.Train set=No 3DPW train set2021.0455.9—————
Pose2MeshTrain set=No 3DPW train set2021.0456.389.5105.3———
PARETraining sets=[COCO]EFT2023.0457.3——20.320.341.7
VIBEApproach category=Video-based2024.0557.691.9————
I2L-MeshNetTrain set=No 3DPW train set2021.0457.793.2110.1———
I2L-MeshNetTraining sets=H36M+MuCo+[COCO]SMPLify-X2023.0457.7——20.621.740.8
PyMAFTraining sets=H36M+MI+[COCO+MPII+LSPET]SPIN2023.0458.9——2123.741.8
SPINTrain set=No 3DPW train set2021.0459.296.9116.4———
SPINTraining sets=H36M+MI+[COCO+MPII+LSPET]SMPLify2023.0459.6——21.624.240.8
BOASource dataset=SURREAL [41], 2D pose estimator=OpenPose [3]2023.0861.7102.5124.7———
DAPASource dataset=SURREAL [41], 2D pose estimator=OpenPose [3]2023.0861.796.6122.8———
BOASource Dataset=SURREAL, Adaptation Scenario=Online, 2D Keypoints Source=OpenPose2023.0861.7102.5124.7———
DynaBOASource dataset=SURREAL [41], 2D pose estimator=OpenPose [3]2023.0862.4109.8139.9———
DynaBOASource Dataset=SURREAL, Adaptation Scenario=Online, 2D Keypoints Source=OpenPose2023.0862.4109.8139.9———
GraphCMRTrain set=No 3DPW train set2021.0470.2—————
DynaBOASource Dataset=Human3.6M, Adaptation Scenario=Online, 2D Keypoint Detector=OpenPose2023.0873135.1168.2———
BOASource Dataset=Human3.6M, Adaptation Scenario=Online, 2D Keypoint Detector=OpenPose2023.0876.2137.6171.8———
HMRTrain set=No 3DPW train set2021.0476.7130————
Base modelPre-trained on=SURREAL [41]2023.0892193.2216.5———
Base modelSource Dataset=SURREAL, Adaptation Scenario=Online, 2D Keypoints Source=OpenPose2023.0892193.2216.5———
Base model (pre-trained on H36M)Source Dataset=Human3.6M, Adaptation Scenario=Online2023.08123.4230.3253.4———