Multi-person pose estimation is the task of estimating the pose of multiple people in one frame.
( Image credit: Human Pose Estimation with TensorFlow )
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Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
3D INSTANCE SEGMENTATION HUMAN PART SEGMENTATION KEYPOINT DETECTION MULTI-HUMAN PARSING MULTI-PERSON POSE ESTIMATION MULTI-TISSUE NUCLEUS SEGMENTATION NUCLEAR SEGMENTATION PANOPTIC SEGMENTATION REAL-TIME OBJECT DETECTION
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.
HigherHRNet even surpasses all top-down methods on CrowdPose test (67. 6% AP), suggesting its robustness in crowded scene.
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 Keypoint Detection on COCO test-dev
The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people.
SOTA for Multi-Person Pose Estimation on WAF
We rethink a well-know bottom-up approach for multi-person pose estimation and propose an improved one.
#3 best model for Multi-Person Pose Estimation on COCO test-dev
In this work we adapt multi-person pose estimation architecture to use it on edge devices.
In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos.
#6 best model for Multi-Person Pose Estimation on MPII Multi-Person
We propose the first direct end-to-end multi-person pose estimation framework, termed DirectPose.
#9 best model for Keypoint Detection on COCO test-dev