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 526550 of 665 papers

TitleStatusHype
Multi-Garment Net: Learning to Dress 3D People from ImagesCode1
Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation0
Predicting 3D Human Dynamics from VideoCode0
Semantic Estimation of 3D Body Shape and Pose using Minimal Cameras0
Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images0
Learning to Dress 3D People in Generative ClothingCode0
Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB ImageCode0
A Unified Deep Framework for Joint 3D Pose Estimation and Action Recognition from a Single RGB Camera0
Sim2real transfer learning for 3D human pose estimation: motion to the rescue0
XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB CameraCode0
Learning Pose Grammar for Monocular 3D Pose Estimation0
HoloPose: Holistic 3D Human Reconstruction In-The-Wild0
Patch-based 3D Human Pose Refinement0
Geometric Pose Affordance: 3D Human Pose with Scene Constraints0
Learnable Triangulation of Human PoseCode0
PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human DigitizationCode0
Exploiting temporal context for 3D human pose estimation in the wildCode0
Convolutional Mesh Regression for Single-Image Human Shape ReconstructionCode0
Absolute Human Pose Estimation with Depth Prediction NetworkCode0
Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density NetworkCode0
Generalizing Monocular 3D Human Pose Estimation in the WildCode0
Expressive Body Capture: 3D Hands, Face, and Body from a Single ImageCode0
Unsupervised 3D Pose Estimation with Geometric Self-Supervision0
Semantic Graph Convolutional Networks for 3D Human Pose RegressionCode1
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations0
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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