Proceedings ArticleDOI
2D Human Pose Estimation: New Benchmark and State of the Art Analysis
Mykhaylo Andriluka,Leonid Pishchulin,Peter V. Gehler,Bernt Schiele +3 more
- pp 3686-3693
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TLDR
A novel benchmark "MPII Human Pose" is introduced that makes a significant advance in terms of diversity and difficulty, a contribution that is required for future developments in human body models.Abstract:
Human pose estimation has made significant progress during the last years. However current datasets are limited in their coverage of the overall pose estimation challenges. Still these serve as the common sources to evaluate, train and compare different models on. In this paper we introduce a novel benchmark "MPII Human Pose" that makes a significant advance in terms of diversity and difficulty, a contribution that we feel is required for future developments in human body models. This comprehensive dataset was collected using an established taxonomy of over 800 human activities [1]. The collected images cover a wider variety of human activities than previous datasets including various recreational, occupational and householding activities, and capture people from a wider range of viewpoints. We provide a rich set of labels including positions of body joints, full 3D torso and head orientation, occlusion labels for joints and body parts, and activity labels. For each image we provide adjacent video frames to facilitate the use of motion information. Given these rich annotations we perform a detailed analysis of leading human pose estimation approaches and gaining insights for the success and failures of these methods.read more
Citations
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Mask R-CNN
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Mask R-CNN
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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.
Book ChapterDOI
Stacked Hourglass Networks for Human Pose Estimation
TL;DR: This work introduces a novel convolutional network architecture for the task of human pose estimation that is described as a “stacked hourglass” network based on the successive steps of pooling and upsampling that are done to produce a final set of predictions.
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.
References
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