3D Human Pose Estimation is the task of estimating the pose of a human from a picture or set of video frames.
( Image credit: 3d-pose-baseline )
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Dynamics of human body skeletons convey significant information for human action recognition.
#2 best model for Skeleton Based Action Recognition on Varying-view RGB-D Action-Skeleton
We start with predicted 2D keypoints for unlabeled video, then estimate 3D poses and finally back-project to the input 2D keypoints.
#12 best model for 3D Human Pose Estimation on Human3.6M
In the second stage, a fully-connected neural network turns the possibly partial (on account of occlusion) 2D pose and 3D pose features for each subject into a complete 3D pose estimate per individual.
#6 best model for 3D Human Pose Estimation on MPI-INF-3DHP
The main objective is to minimize the reprojection loss of keypoints, which allow our model to be trained using images in-the-wild that only have ground truth 2D annotations.
#4 best model for 3D Human Pose Estimation on 3DPW (using extra training data)
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 present two novel solutions for multi-view 3D human pose estimation based on new learnable triangulation methods that combine 3D information from multiple 2D views.
SOTA for 3D Human Pose Estimation on Human3.6M (using extra training data)
Deep learning for predicting or generating 3D human pose sequences is an active research area.
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 use the new method, SMPLify-X, to fit SMPL-X to both controlled images and images in the wild.