Pose Estimation is a general problem in Computer Vision where we detect the position and orientation of an object.
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To the best of our knowledge, this is the first paper to propose an online human pose tracking framework in a top-down fashion.
SOTA for Pose Tracking on PoseTrack2017
We observe that recent innovation in this area mainly focuses on new techniques that explicitly address the generalization issue when using this dataset, because this database is constructed in a highly controlled environment with limited human subjects and background variations.
We argue that 3D human pose estimation from a monocular input is an inverse problem where multiple feasible solutions can exist.
The proposed approach achieves superior results to existing single-model networks on COCO object detection.
We propose a new bottom-up method for multi-person 2D human pose estimation that is particularly well suited for urban mobility such as self-driving cars and delivery robots.
#2 best model for Keypoint Detection on COCO (Test AP metric)
Unfortunately, this research has primarily focused on distributions defined in Euclidean space, ruling out the usage of one of the most influential class of spaces with non-trivial topologies: Lie groups.
Training accurate 3D human pose estimators requires large amount of 3D ground-truth data which is costly to collect.
#2 best model for 3D Human Pose Estimation on Human3.6M