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 1–10 of 665 papers

TitleStatusHype
TRAM: Global Trajectory and Motion of 3D Humans from in-the-wild VideosCode4
BlazePose: On-device Real-time Body Pose trackingCode4
PromptHMR: Promptable Human Mesh RecoveryCode3
TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose RepresentationCode3
Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single ShotCode3
WHAM: Reconstructing World-grounded Humans with Accurate 3D MotionCode3
SMPLer-X: Scaling Up Expressive Human Pose and Shape EstimationCode3
TRACE: 5D Temporal Regression of Avatars with Dynamic Cameras in 3D EnvironmentsCode3
Humans in 4D: Reconstructing and Tracking Humans with TransformersCode3
HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-body Mesh RecoveryCode3
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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