Pose Tracking
60 papers with code • 3 benchmarks • 9 datasets
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
Benchmarks
These leaderboards are used to track progress in Pose Tracking
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Libraries
Use these libraries to find Pose Tracking models and implementationsDatasets
PoseTrack
10,000 People - Human Pose Recognition Data
HOI4D
VR-Folding
Drunkard's Dataset
InfiniteRep
Most implemented papers
POSE-ID-on—A Novel Framework for Artwork Pose Clustering
In this work, we focus our attention on the similarity among works of art based on human poses and the actions they represent, moving from the concept of Pathosformel in Aby Warburg.
SRT3D: A Sparse Region-Based 3D Object Tracking Approach for the Real World
Finally, we use a pre-rendered sparse viewpoint model to create a joint posterior probability for the object pose.
ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity Tracking
In this work, we introduce ROFT, a Kalman filtering approach for 6D object pose and velocity tracking from a stream of RGB-D images.
You Only Demonstrate Once: Category-Level Manipulation from Single Visual Demonstration
The canonical object representation is learned solely in simulation and then used to parse a category-level, task trajectory from a single demonstration video.
Single camera pose estimation using Bayesian filtering and Kinect motion priors
This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information.
Detect-and-Track: Efficient Pose Estimation in Videos
facebookresearch/DetectAndTrack
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CVPR 2018
This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video.
Pose Flow: Efficient Online Pose Tracking
Multi-person articulated pose tracking in unconstrained videos is an important while challenging problem.
A Region-based Gauss-Newton Approach to Real-Time Monocular Multiple Object Tracking
We propose an algorithm for real-time 6DOF pose tracking of rigid 3D objects using a monocular RGB camera.
Deep Model-Based 6D Pose Refinement in RGB
We present a novel approach for model-based 6D pose refinement in color data.
PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking
NVlabs/PoseRBPF
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In this work, we formulate the 6D object pose tracking problem in the Rao-Blackwellized particle filtering framework, where the 3D rotation and the 3D translation of an object are decoupled.
