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 451475 of 665 papers

TitleStatusHype
TesseTrack: End-to-End Learnable Multi-Person Articulated 3D Pose Tracking0
SVMAC: Unsupervised 3D Human Pose Estimation from a Single Image with Single-view-multi-angle Consistency0
3D Human Pose Estimation Based on 2D-3D Consistency with Synchronized Adversarial Training0
Weakly-supervised 3D Human Pose Estimation with Cross-view U-shaped Graph Convolutional Network0
Heuristic Weakly Supervised 3D Human Pose EstimationCode0
Adapted Human Pose: Monocular 3D Human Pose Estimation with Zero Real 3D Pose DataCode0
3D Human Pose Regression using Graph Convolutional NetworkCode0
TriPose: A Weakly-Supervised 3D Human Pose Estimation via Triangulation from Video0
KAMA: 3D Keypoint Aware Body Mesh Articulation0
Skeletor: Skeletal Transformers for Robust Body-Pose Estimation0
Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective0
Deep Monocular 3D Human Pose Estimation via Cascaded Dimension-Lifting0
SimPoE: Simulated Character Control for 3D Human Pose Estimation0
Self-Attentive 3D Human Pose and Shape Estimation from Videos0
Probabilistic 3D Human Shape and Pose Estimation from Multiple Unconstrained Images in the Wild0
PC-HMR: Pose Calibration for 3D Human Mesh Recovery from 2D Images/Videos0
Enhanced 3D Human Pose Estimation from Videos by using Attention-Based Neural Network with Dilated Convolutions0
On the role of depth predictions for 3D human pose estimation0
PandaNet : Anchor-Based Single-Shot Multi-Person 3D Pose Estimation0
Towards Alleviating the Modeling Ambiguity of Unsupervised Monocular 3D Human Pose Estimation0
SMPLy Benchmarking 3D Human Pose Estimation in the Wild0
Towards Locality Similarity Preserving to 3D Human Pose Estimation0
Temporal Representation Learning on Monocular Videos for 3D Human Pose Estimation0
Leveraging Temporal Joint Depths for Improving 3D Human Pose Estimation in Video0
Exploring Severe Occlusion: Multi-Person 3D Pose Estimation with Gated Convolution0
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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