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Authors: Takashi Shimizu ; Fumihiko Sakaue and Jun Sato

Affiliation: Nagoya Institute of Technology, Japan

ISBN: 978-989-758-290-5

Keyword(s): Human Poses, Camera Motions, CNN, RNN, LSTM, Deep Learning.

Abstract: In this paper, we propose a novel method for recovering 3D human poses and camera motions from sequential images by using CNN and LSTM. The human pose estimation from deep learning has been studied extensively in recent years. However, the existing methods aim to classify 2D human motions in images. Although some methods have been proposed for recovering 3D human poses recently, they only considered single frame poses, and sequential properties of human actions were not used efficiently. Furthermore, the existing methods recover only 3D poses relative to the viewpoints. In this paper, we propose a method for recovering 3D human poses and 3D camera motions simultaneously from sequential input images. In our network, CNN is combined with LSTM, so that the proposed network can learn sequential properties of 3D human poses and camera motions efficiently. The efficiency of the proposed method is evaluated by using real images as well as synthetic images.

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Paper citation in several formats:
Shimizu, T.; Sakaue, F. and Sato, J. (2018). Recovering 3D Human Poses and Camera Motions from Deep Sequence.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, ISBN 978-989-758-290-5, pages 393-398. DOI: 10.5220/0006718603930398

@conference{visapp18,
author={Takashi Shimizu. and Fumihiko Sakaue. and Jun Sato.},
title={Recovering 3D Human Poses and Camera Motions from Deep Sequence},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP,},
year={2018},
pages={393-398},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006718603930398},
isbn={978-989-758-290-5},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP,
TI - Recovering 3D Human Poses and Camera Motions from Deep Sequence
SN - 978-989-758-290-5
AU - Shimizu, T.
AU - Sakaue, F.
AU - Sato, J.
PY - 2018
SP - 393
EP - 398
DO - 10.5220/0006718603930398

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