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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Although significant improvement has been achieved in 3D human pose estimation, most of the previous methods only consider a single-person case.
It consists of two separate steps: (1) estimating the 2D poses in multi-view images and (2) recovering the 3D poses from the multi-view 2D poses.
SOTA for 3D Human Pose Estimation on Human3.6M (using extra training data)
Although existing CNN-based temporal frameworks attempt to address the sensitivity and drift problems by concurrently processing all input frames in the sequence, the existing state-of-the-art CNN-based framework is limited to 3d pose estimation of a single frame from a sequential input.
#6 best model for 3D Human Pose Estimation on Human3.6M
Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case.
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.
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.
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.