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

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Showing 501–550 of 665 papers

TitleStatusHype
ActiveMoCap: Optimized Viewpoint Selection for Active Human Motion CaptureCode0
Multi-task Deep Learning for Real-Time 3D Human Pose Estimation and Action RecognitionCode0
VIBE: Video Inference for Human Body Pose and Shape EstimationCode1
DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image—0
Consensus-based Optimization for 3D Human Pose Estimation in Camera CoordinatesCode0
Single-shot 3D multi-person pose estimation in complex images—0
Chirality Nets for Human Pose RegressionCode0
HEMlets Pose: Learning Part-Centric Heatmap Triplets for Accurate 3D Human Pose Estimation—0
PoseLifter: Absolute 3D human pose lifting network from a single noisy 2D human poseCode0
Not All Parts Are Created Equal: 3D Pose Estimation by Modeling Bi-Directional Dependencies of Body Parts—0
On Boosting Single-Frame 3D Human Pose Estimation via Monocular Videos—0
Exploiting Spatial-Temporal Relationships for 3D Pose Estimation via Graph Convolutional NetworksCode0
Occlusion-Aware Networks for 3D Human Pose Estimation in Video—0
Optimizing Network Structure for 3D Human Pose Estimation—0
DenseRaC: Joint 3D Pose and Shape Estimation by Dense Render-and-Compare—0
Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the LoopCode1
Multi-Person 3D Human Pose Estimation from Monocular Images—0
Making the Invisible Visible: Action Recognition Through Walls and Occlusions—0
Weakly-Supervised 3D Pose Estimation from a Single Image using Multi-View Consistency—0
Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsense—0
Cross View Fusion for 3D Human Pose EstimationCode0
Shape-Aware Human Pose and Shape Reconstruction Using Multi-View Images—0
Trajectory Space Factorization for Deep Video-Based 3D Human Pose EstimationCode0
Human Mesh Recovery from Monocular Images via a Skeleton-disentangled RepresentationCode0
Resolving 3D Human Pose Ambiguities with 3D Scene ConstraintsCode0
Multi-Garment Net: Learning to Dress 3D People from ImagesCode1
Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation—0
Predicting 3D Human Dynamics from VideoCode0
Semantic Estimation of 3D Body Shape and Pose using Minimal Cameras—0
Moulding Humans: Non-parametric 3D Human Shape Estimation from Single Images—0
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 Camera—0
Sim2real transfer learning for 3D human pose estimation: motion to the rescue—0
XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB CameraCode0
Learning Pose Grammar for Monocular 3D Pose Estimation—0
HoloPose: Holistic 3D Human Reconstruction In-The-Wild—0
Patch-based 3D Human Pose Refinement—0
Geometric Pose Affordance: 3D Human Pose with Scene Constraints—0
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-Supervision—0
Semantic Graph Convolutional Networks for 3D Human Pose RegressionCode1
In the Wild Human Pose Estimation Using Explicit 2D Features and Intermediate 3D Representations—0
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All datasets3DPWMPI-INF-3DHPHuman3.6MHumanEva-IH3WBTotal CaptureEMDBAGORAPanopticSurreal3D Poses in the Wild ChallengeAIST++

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Simple-baselinePA-MPJPE157—Unverified
2HMRMPJPE130—Unverified
3BMPMPVPE119.3—Unverified
4SPINMPVPE116.4—Unverified
5Wenshuo et a;.MPVPE112.6—Unverified
6TCMR (T=16 w/o 3DPW)MPVPE111.5—Unverified
7CHOMPMPVPE110.1—Unverified
8PC-HMRMPVPE108.6—Unverified
93DCrowdNetMPVPE108.5—Unverified
10SMPLifyPA-MPJPE106.8—Unverified
#ModelMetricClaimedVerifiedStatus
1VNect (Augm.)MPJPE124.7—Unverified
2HMRMPJPE124.2—Unverified
3Single-Shot Multi-PersonMPJPE122.2—Unverified
4MehtaMPJPE117.6—Unverified
5PONetMPJPE115—Unverified
6Pose Consensus (monocular)MPJPE112.1—Unverified
7GeoRep (fully-supervised)MPJPE110.8—Unverified
8XFormer (HRNet)MPJPE109.8—Unverified
9EpipolarPose (fully-supervised)MPJPE108.99—Unverified
10SPINMPJPE105.2—Unverified