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OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
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OpenPose is released, the first open-source realtime system for multi-person 2D pose detection, including body, foot, hand, and facial keypoints, and the first combined body and foot keypoint detector, based on an internal annotated foot dataset.Abstract:
Realtime multi-person 2D pose estimation is a key component in enabling machines to have an understanding of people in images and videos. In this work, we present a realtime approach to detect the 2D pose of multiple people in an image. The proposed method uses a nonparametric representation, which we refer to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image. This bottom-up system achieves high accuracy and realtime performance, regardless of the number of people in the image. In previous work, PAFs and body part location estimation were refined simultaneously across training stages. We demonstrate that a PAF-only refinement rather than both PAF and body part location refinement results in a substantial increase in both runtime performance and accuracy. We also present the first combined body and foot keypoint detector, based on an internal annotated foot dataset that we have publicly released. We show that the combined detector not only reduces the inference time compared to running them sequentially, but also maintains the accuracy of each component individually. This work has culminated in the release of OpenPose, the first open-source realtime system for multi-person 2D pose detection, including body, foot, hand, and facial keypoints.read more
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Journal ArticleDOI
Human Pose Estimation-Based Real-Time Gait Analysis Using Convolutional Neural Network
TL;DR: This work presents an approach where human pose estimation is combined with a CNN for classification between normal and abnormal gait of a human with an ability to provide information about the detected abnormalities form an extracted skeletal image in real-time.
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Weakly Supervised Adversarial Learning for 3D Human Pose Estimation from Point Clouds
TL;DR: This work presents a weakly supervised adversarial learning framework for 3D human pose estimation from point clouds and adopts 2D CNN to extract 2D human joints from the input depth image, which helps in obtaining the initial 3D humans joints and selecting effective sampling points that could reduce the computation cost of 3Dhuman pose regression using point clouds network.
Proceedings ArticleDOI
Reducing Footskate in Human Motion Reconstruction with Ground Contact Constraints
TL;DR: A neural network based detector for localizing ground contact events of human feet is presented and used to impose a physical constraint for optimization of the whole human dynamics in a video.
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