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.
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We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel.
Ranked #1 on Keypoint Detection on COCO test-dev
This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video.
Ranked #5 on Pose Tracking on PoseTrack2017 (using extra training data)
To the best of our knowledge, this is the first paper to propose an online human pose tracking framework in a top-down fashion.
Ranked #1 on Pose Tracking on PoseTrack2017 (MAP metric)
In this work, we introduce the challenging problem of joint multi-person pose estimation and tracking of an unknown number of persons in unconstrained videos.
Ranked #1 on Pose Tracking on Multi-Person PoseTrack
We introduce multigrid Predictive Filter Flow (mgPFF), a framework for unsupervised learning on videos.
Tracking the 6D pose of objects in video sequences is important for robot manipulation.
Ranked #2 on 6D Pose Estimation on YCB-Video
We propose an algorithm for real-time 6DOF pose tracking of rigid 3D objects using a monocular RGB camera.