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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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References
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Proceedings ArticleDOI
Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation
Sam Johnson,Mark Everingham +1 more
TL;DR: A new annotated database of challenging consumer images is introduced, an order of magnitude larger than currently available datasets, and over 50% relative improvement in pose estimation accuracy over a state-of-the-art method is demonstrated.
Book ChapterDOI
DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model
Eldar Insafutdinov,Leonid Pishchulin,Bjoern Andres,Mykhaylo Andriluka,Mykhaylo Andriluka,Bernt Schiele +5 more
TL;DR: In this article, the authors proposed an improved body part detector that generates effective bottom-up proposals for body parts, image-conditioned pairwise terms that allow to assemble the proposals into a variable number of consistent body part configurations, and an incremental optimization strategy that explores the search space more efficiently thus leading both to better performance and significant speedup factors.
Journal ArticleDOI
Articulated Human Detection with Flexible Mixtures of Parts
Yi Yang,Deva Ramanan +1 more
TL;DR: A general, flexible mixture model that jointly captures spatial relations between part locations and co-occurrence Relations between part mixtures, augmenting standard pictorial structure models that encode just spatial relations.
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Efficient Object Localization Using Convolutional Networks
TL;DR: A novel architecture which includes an efficient `position refinement' model that is trained to estimate the joint offset location within a small region of the image to achieve improved accuracy in human joint location estimation is introduced.
Journal ArticleDOI
VNect: real-time 3D human pose estimation with a single RGB camera
Dushyant Mehta,Srinath Sridhar,Oleksandr Sotnychenko,Helge Rhodin,Mohammad Shafiei,Hans-Peter Seidel,Weipeng Xu,Dan Casas,Christian Theobalt +8 more
TL;DR: In this paper, a fully-convolutional pose formulation was proposed to regress 2D and 3D joint positions jointly in real-time and does not require tightly cropped input frames.