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Erschienen in:
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information

verfasst von : Sungheon Park, Jihye Hwang, Nojun Kwak

Erschienen in: Computer Vision – ECCV 2016 Workshops

Verlag: Springer International Publishing

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Abstract

While there has been a success in 2D human pose estimation with convolutional neural networks (CNNs), 3D human pose estimation has not been thoroughly studied. In this paper, we tackle the 3D human pose estimation task with end-to-end learning using CNNs. Relative 3D positions between one joint and the other joints are learned via CNNs. The proposed method improves the performance of CNN with two novel ideas. First, we added 2D pose information to estimate a 3D pose from an image by concatenating 2D pose estimation result with the features from an image. Second, we have found that more accurate 3D poses are obtained by combining information on relative positions with respect to multiple joints, instead of just one root joint. Experimental results show that the proposed method achieves comparable performance to the state-of-the-art methods on Human 3.6m dataset.

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Vorheriges Kapitel A CNN Cascade for Landmark Guided Semantic Part Segmentation
Nächstes Kapitel Overcoming Occlusion with Inverse Graphics
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Metadaten
Titel
3D Human Pose Estimation Using Convolutional Neural Networks with 2D Pose Information
verfasst von
Sungheon Park
Jihye Hwang
Nojun Kwak
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016 Workshops

Print ISBN: 978-3-319-49408-1

Electronic ISBN: 978-3-319-49409-8

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-49409-8_15

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