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 101125 of 665 papers

TitleStatusHype
Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with TransformersCode1
Body Meshes as PointsCode1
3D Human Pose Estimation with Spatial and Temporal TransformersCode1
Efficient Human Pose Estimation via 3D Event Point CloudCode1
Dual networks based 3D Multi-Person Pose Estimation from Monocular VideoCode1
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution EstimationCode1
GLAMR: Global Occlusion-Aware Human Mesh Recovery with Dynamic CamerasCode1
Efficient Domain Adaptation via Generative Prior for 3D Infant Pose EstimationCode1
GFPose: Learning 3D Human Pose Prior with Gradient FieldsCode1
Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion PredictionCode1
CrossFormer: Cross Spatio-Temporal Transformer for 3D Human Pose EstimationCode1
EgoPoseFormer: A Simple Baseline for Stereo Egocentric 3D Human Pose EstimationCode1
CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the WildCode1
AdaFuse: Adaptive Multiview Fusion for Accurate Human Pose Estimation in the WildCode1
GLA-GCN: Global-local Adaptive Graph Convolutional Network for 3D Human Pose Estimation from Monocular VideoCode1
Capturing the motion of every joint: 3D human pose and shape estimation with independent tokensCode1
Cascaded deep monocular 3D human pose estimation with evolutionary training dataCode1
Encoder-decoder with Multi-level Attention for 3D Human Shape and Pose EstimationCode1
Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose EstimationCode1
AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion GenerationCode1
A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose EstimationCode1
Coarse-to-Fine Volumetric Prediction for Single-Image 3D Human PoseCode1
Co-Evolution of Pose and Mesh for 3D Human Body Estimation from VideoCode1
Combining Implicit Function Learning and Parametric Models for 3D Human ReconstructionCode1
Anatomy-guided domain adaptation for 3D in-bed human pose estimationCode1
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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