Multi-person pose estimation is the task of estimating the pose of multiple people in one frame.
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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.
Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case.
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.
#4 best model for Keypoint Detection on COCO
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.
SOTA for Multi-Person Pose Estimation on COCO
A complex deep learning model with high accuracy runs slowly on resource-limited devices, while a light-weight model that runs much faster loses accuracy.
In this paper, we propose a human pose refinement network that estimates a refined pose from a tuple of an input image and input pose.
SOTA for Multi-Person Pose Estimation on COCO (Validation AP metric )
In this paper, we propose a novel and efficient method to tackle the problem of pose estimation in the crowd and a new dataset to better evaluate algorithms.
In this work we adapt multi-person pose estimation architecture to use it on edge devices.
In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose estimation architecture that combines a multi-task model with a novel assignment method.
#3 best model for Multi-Person Pose Estimation on COCO