Multi-person pose estimation is the task of estimating the pose of multiple people in one frame.
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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.
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
In this paper, we present a novel network structure called Cascaded Pyramid Network (CPN) which targets to relieve the problem from these "hard" keypoints.
#2 best model for Multi-Person Pose Estimation on COCO
This paper proposes a new Generative Partition Network (GPN) to address the challenging multi-person pose estimation problem.
SOTA for Multi-Person Pose Estimation on WAF (AP metric )
In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos.
#5 best model for Multi-Person Pose Estimation on MPII Multi-Person
In this paper, we propose a novel regional multi-person pose estimation (RMPE) framework to facilitate pose estimation in the presence of inaccurate human bounding boxes.