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.
Ranked #1 on Real-Time Object Detection on COCO minival (MAP metric)
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.
Ranked #1 on Multi-Person Pose Estimation on MPII Multi-Person
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.
Ranked #1 on Keypoint Detection on COCO test-dev
We rethink a well-know bottom-up approach for multi-person pose estimation and propose an improved one.
Ranked #3 on Multi-Person Pose Estimation on COCO test-dev
In this work we adapt multi-person pose estimation architecture to use it on edge devices.
The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people.
Ranked #1 on Multi-Person Pose Estimation on WAF
We propose the first direct end-to-end multi-person pose estimation framework, termed DirectPose.
Ranked #9 on Keypoint Detection on COCO test-dev
In this paper we propose an approach for articulated tracking of multiple people in unconstrained videos.
Ranked #7 on Multi-Person Pose Estimation on MPII Multi-Person
To tackle this problem, we propose an efficient attention mechanism - Pose Refine Machine (PRM) to make a trade-off between local and global representations in output features and further refine the keypoint locations.