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2D Human Pose Estimation: New Benchmark and State of the Art Analysis
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.
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Citations
Cites
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“6M” [10] that includes images and 3D poses of people but are captured in the controlled indoor environments, whereas our dataset includes real-world images but provides 2D poses only.”
Background
Cited by
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“We evaluate our method on two benchmarks for multi-person pose estimation: (1) the MPII human multi-person dataset [2] and (2) the MSCOCO 2016 keypoints challenge dataset [21].”
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Metadata
- Authors
- Andriluka, M., Pishchulin, L., Gehler, P., & Schiele, B.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 3686-3693
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2080873731
- Cited by
- 2,688
- References
- 3
- FWCI
- 41.09
- Type
- conference paper
- Language
- en
- Open access
- closed
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