Proceedings ArticleDOI
Strong Appearance and Expressive Spatial Models for Human Pose Estimation
Leonid Pishchulin,Mykhaylo Andriluka,Peter V. Gehler,Bernt Schiele +3 more
- pp 3487-3494
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TLDR
This paper demonstrates that even a basic tree-structure spatial human body model achieves state-of-the-art performance when augmented with the proper appearance representation, and shows that the combination of the best performing appearance model with a flexible image-conditioned spatial model achieves the best result.Abstract:
Typical approaches to articulated pose estimation combine spatial modelling of the human body with appearance modelling of body parts. This paper aims to push the state-of-the-art in articulated pose estimation in two ways. First we explore various types of appearance representations aiming to substantially improve the body part hypotheses. And second, we draw on and combine several recently proposed powerful ideas such as more flexible spatial models as well as image-conditioned spatial models. In a series of experiments we draw several important conclusions: (1) we show that the proposed appearance representations are complementary, (2) we demonstrate that even a basic tree-structure spatial human body model achieves state-of-the-art performance when augmented with the proper appearance representation, and (3) we show that the combination of the best performing appearance model with a flexible image-conditioned spatial model achieves the best result, significantly improving over the state of the art, on the ``Leeds Sports Poses'' and ``Parse'' benchmarks.read more
Citations
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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.
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.
Proceedings ArticleDOI
2D Human Pose Estimation: New Benchmark and State of the Art Analysis
TL;DR: 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.
Posted Content
Stacked Hourglass Networks for Human Pose Estimation
TL;DR: Stacked hourglass networks as mentioned in this paper were proposed for human pose estimation, where features are processed across all scales and consolidated to best capture the various spatial relationships associated with the body, and repeated bottom-up, top-down processing with intermediate supervision is critical to improving the performance of the network.
Posted Content
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
TL;DR: This paper proposes a new hybrid architecture that consists of a deep Convolu-tional Network and a Markov Random Field and shows how this architecture is successfully applied to the challenging problem of articulated human pose estimation in monocular images.
References
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Proceedings ArticleDOI
Real-time human pose recognition in parts from single depth images
Jamie Shotton,Andrew Fitzgibbon,Mat Cook,Toby Sharp,Mark J. Finocchio,Richard E. Moore,Alex Aben-Athar Kipman,Andrew Blake +7 more
TL;DR: This work takes an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem into a simpler per-pixel classification problem, and generates confidence-scored 3D proposals of several body joints by reprojecting the classification result and finding local modes.
Proceedings ArticleDOI
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