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 301325 of 665 papers

TitleStatusHype
MPT: Mesh Pre-Training with Transformers for Human Pose and Mesh ReconstructionCode0
Monocular Total Capture: Posing Face, Body, and Hands in the WildCode0
Integral Human Pose RegressionCode0
Mirror-Aware Neural HumansCode0
3D Human Pose Estimation with Siamese Equivariant EmbeddingCode0
MetaPose: Fast 3D Pose from Multiple Views without 3D SupervisionCode0
3D Human Pose Estimation with Relational NetworksCode0
MHEntropy: Entropy Meets Multiple Hypotheses for Pose and Shape RecoveryCode0
Measuring Physical Plausibility of 3D Human Poses Using Physics SimulationCode0
Decanus to Legatus: Synthetic training for 2D-3D human pose liftingCode0
A Multi-view RGB-D Approach for Human Pose Estimation in Operating RoomsCode0
3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN RefinementCode0
AMPose: Alternately Mixed Global-Local Attention Model for 3D Human Pose EstimationCode0
3D-CODED : 3D Correspondences by Deep DeformationCode0
ManiPose: Manifold-Constrained Multi-Hypothesis 3D Human Pose EstimationCode0
Human Mesh Recovery from Monocular Images via a Skeleton-disentangled RepresentationCode0
Joint 3D Human Shape Recovery and Pose Estimation from a Single Image with Bilayer GraphCode0
Learnable Triangulation of Human PoseCode0
Lifting from the Deep: Convolutional 3D Pose Estimation from a Single ImageCode0
HSTFormer: Hierarchical Spatial-Temporal Transformers for 3D Human Pose EstimationCode0
Cross View Fusion for 3D Human Pose EstimationCode0
3D Human Pose Estimation with 2D Marginal HeatmapsCode0
HPERL: 3D Human Pose Estimation from RGB and LiDARCode0
How Robust is 3D Human Pose Estimation to Occlusion?Code0
Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge NetworksCode0
Show:102550
← PrevPage 13 of 27Next →

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