Multi-person pose estimation is the task of estimating the pose of multiple people in one frame.
( Image credit: Human Pose Estimation with TensorFlow )
|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
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 Keypoint Detection on MPII Multi-Person
Interestingly, we found that the process of decoding the predicted heatmaps into the final joint coordinates in the original image space is surprisingly significant for human pose estimation performance, which nevertheless was not recognised before.
Ranked #1 on Multi-Person Pose Estimation on COCO (using extra training data)
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 #8 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 #10 on Keypoint Detection on COCO test-dev