SOTAVerified

3D Human Pose Estimation

3D Human Pose Estimation is a computer vision task that involves estimating the 3D positions and orientations of body joints and bones from 2D images or videos. The goal is to reconstruct the 3D pose of a person in real-time, which can be used in a variety of applications, such as virtual reality, human-computer interaction, and motion analysis.

Papers

Showing 281290 of 665 papers

TitleStatusHype
3D Human Pose Regression using Graph Convolutional NetworkCode0
MHEntropy: Entropy Meets Multiple Hypotheses for Pose and Shape RecoveryCode0
MetaPose: Fast 3D Pose from Multiple Views without 3D SupervisionCode0
3D human pose estimation from depth maps using a deep combination of posesCode0
DeProPose: Deficiency-Proof 3D Human Pose Estimation via Adaptive Multi-View FusionCode0
ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose EstimationCode0
Measuring Physical Plausibility of 3D Human Poses Using Physics SimulationCode0
DensePose: Dense Human Pose Estimation In The WildCode0
3D Human Pose Machines with Self-supervised LearningCode0
Mirror-Aware Neural HumansCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Simple-baselinePA-MPJPE157Unverified
2HMRMPJPE130Unverified
3BMPMPVPE119.3Unverified
4SPINMPVPE116.4Unverified
5Wenshuo et a;.MPVPE112.6Unverified
6TCMR (T=16 w/o 3DPW)MPVPE111.5Unverified
7CHOMPMPVPE110.1Unverified
8PC-HMRMPVPE108.6Unverified
93DCrowdNetMPVPE108.5Unverified
10SMPLifyPA-MPJPE106.8Unverified
#ModelMetricClaimedVerifiedStatus
1VNect (Augm.)MPJPE124.7Unverified
2HMRMPJPE124.2Unverified
3Single-Shot Multi-PersonMPJPE122.2Unverified
4MehtaMPJPE117.6Unverified
5PONetMPJPE115Unverified
6Pose Consensus (monocular)MPJPE112.1Unverified
7GeoRep (fully-supervised)MPJPE110.8Unverified
8XFormer (HRNet)MPJPE109.8Unverified
9EpipolarPose (fully-supervised)MPJPE108.99Unverified
10SPINMPJPE105.2Unverified