3D Human Pose Estimation is the task of estimating the pose of a human from a picture or set of video frames.
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Dynamics of human body skeletons convey significant information for human action recognition.
We start with predicted 2D keypoints for unlabeled video, then estimate 3D poses and finally back-project to the input 2D keypoints.
#7 best model for 3D Human Pose Estimation on Human3.6M
Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels.
We propose a weakly-supervised transfer learning method that uses mixed 2D and 3D labels in a unified deep neutral network that presents two-stage cascaded structure.
#4 best model for 3D Human Pose Estimation on Geometric Pose Affordance
We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks.
#19 best model for 3D Human Pose Estimation on Human3.6M
We present the first method to capture the 3D total motion of a target person from a monocular view input.
Training accurate 3D human pose estimators requires large amount of 3D ground-truth data which is costly to collect.
#10 best model for 3D Human Pose Estimation on Human3.6M