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Yaser Sheikh
Researcher at Facebook
Publications - 180
Citations - 26313
Yaser Sheikh is an academic researcher from Facebook. The author has contributed to research in topics: Rendering (computer graphics) & Motion capture. The author has an hindex of 50, co-authored 172 publications receiving 19264 citations. Previous affiliations of Yaser Sheikh include Toyota Motor Engineering & Manufacturing North America & Carnegie Mellon University.
Papers
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Proceedings ArticleDOI
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
TL;DR: Part Affinity Fields (PAFs) as discussed by the authors uses a nonparametric representation to learn to associate body parts with individuals in the image and achieves state-of-the-art performance on the MPII Multi-Person benchmark.
Posted Content
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
TL;DR: This work presents an approach to efficiently detect the 2D pose of multiple people in an image using a nonparametric representation, which it refers to as Part Affinity Fields (PAFs), to learn to associate body parts with individuals in the image.
Journal ArticleDOI
OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
TL;DR: OpenPose as mentioned in this paper uses Part Affinity Fields (PAFs) to learn to associate body parts with individuals in the image, which achieves high accuracy and real-time performance.
Proceedings ArticleDOI
Convolutional Pose Machines
TL;DR: In this paper, a convolutional network is incorporated into the pose machine framework for learning image features and image-dependent spatial models for the task of pose estimation, which can implicitly model long-range dependencies between variables in structured prediction tasks such as articulated pose estimation.
Posted Content
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
TL;DR: 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.