SOTAVerified

Pose Tracking

Pose Tracking is the task of estimating multi-person human poses in videos and assigning unique instance IDs for each keypoint across frames. Accurate estimation of human keypoint-trajectories is useful for human action recognition, human interaction understanding, motion capture and animation.

Source: LightTrack: A Generic Framework for Online Top-Down Human Pose Tracking

Papers

Showing 125 of 191 papers

TitleStatusHype
FoundationPose: Unified 6D Pose Estimation and Tracking of Novel ObjectsCode4
BlazePose: On-device Real-time Body Pose trackingCode4
Keypoint Promptable Re-IdentificationCode3
Humans in 4D: Reconstructing and Tracking Humans with TransformersCode3
BundleSDF: Neural 6-DoF Tracking and 3D Reconstruction of Unknown ObjectsCode3
RGBTrack: Fast, Robust Depth-Free 6D Pose Estimation and TrackingCode2
ESVO2: Direct Visual-Inertial Odometry with Stereo Event CamerasCode2
IMU-Aided Event-based Stereo Visual OdometryCode2
AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion SensingCode2
Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo CamerasCode2
Keypoint-Based Category-Level Object Pose Tracking from an RGB Sequence with Uncertainty EstimationCode2
You Only Demonstrate Once: Category-Level Manipulation from Single Visual DemonstrationCode2
SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and O(T) ComplexityCode1
Embracing Dynamics: Dynamics-aware 4D Gaussian Splatting SLAMCode1
DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera ScenariosCode1
GS-EVT: Cross-Modal Event Camera Tracking based on Gaussian SplattingCode1
SRPose: Two-view Relative Pose Estimation with Sparse KeypointsCode1
HO-Cap: A Capture System and Dataset for 3D Reconstruction and Pose Tracking of Hand-Object InteractionCode1
High-Fidelity SLAM Using Gaussian Splatting with Rendering-Guided Densification and Regularized OptimizationCode1
VideoMAC: Video Masked Autoencoders Meet ConvNetsCode1
Towards Real-World Aerial Vision Guidance with Categorical 6D Pose TrackerCode1
APTv2: Benchmarking Animal Pose Estimation and Tracking with a Large-scale Dataset and BeyondCode1
PACE: A Large-Scale Dataset with Pose Annotations in Cluttered EnvironmentsCode1
Deep Event Visual OdometryCode1
Multimodal video and IMU kinematic dataset on daily life activities using affordable devices (VIDIMU)Code1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DetTrackMOTA64.09Unverified
2KeyTrackMOTA61.15Unverified
3LightTrackMOTA58.01Unverified
4HRNet-W48 COCOMOTA57.93Unverified
5MSRA (FlowTrack)MOTA57.81Unverified
6TML++ (MIPAL)MOTA54.46Unverified
7STAFMOTA53.81Unverified
8ProTrackerMOTA51.82Unverified
9PoseFlowMOTA50.98Unverified
10PoseTrackMOTA48.37Unverified
#ModelMetricClaimedVerifiedStatus
1DetTrackMOTA64.3Unverified
2UniTrackMOTA63.5Unverified
34DHumans + ViTDetMOTA61.9Unverified
4MSRAMOTA61.37Unverified
5TML++ (MIPAL)MOTA54.86Unverified
#ModelMetricClaimedVerifiedStatus
1PoseTrackMOTA28.2Unverified