This paper proposes a framework for automatically annotating the keypoints of a human body in images for learning 2D pose estimation models. Ground-truth annotations for supervised learning are difficult and cumbersome in most machine vision tasks. While considerable contributions in the community provide us a huge number of pose-annotated images, all of them mainly focus on people wearing common clothes, which are relatively easy to annotate the body keypoints. This paper, on the other hand, focuses on annotating people wearing loose-fitting clothes (e.g., Japanese Kimono) that occlude many body keypoints. In order to automatically and correctly annotate these people, we divert the 3D coordinates of the keypoints observed without loose-fitting clothes, which can be captured by a motion capture system (MoCap). These 3D keypoints are projected to an image where the body pose under loose-fitting clothes is similar to the one captured by the MoCap. Pose similarity between bodies with and without loose-fitting clothes is evaluated with 3D geometric configurations of MoCap markers that are visible even with loose-fitting clothes (e.g., markers on the head, wrists, and ankles). Experimental results validate the effectiveness of our proposed framework for human pose estimation.
Takuya MATSUMOTO
Toyota Technological Institute
Kodai SHIMOSATO
Toyota Technological Institute
Takahiro MAEDA
Toyota Technological Institute
Tatsuya MURAKAMI
Toyota Technological Institute
Koji MURAKOSO
Toei Digital Center
Kazuhiko MINO
Toei Digital Center
Norimichi UKITA
Toyota Technological Institute
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Takuya MATSUMOTO, Kodai SHIMOSATO, Takahiro MAEDA, Tatsuya MURAKAMI, Koji MURAKOSO, Kazuhiko MINO, Norimichi UKITA, "Human Pose Annotation Using a Motion Capture System for Loose-Fitting Clothes" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 6, pp. 1257-1264, June 2020, doi: 10.1587/transinf.2019MVP0007.
Abstract: This paper proposes a framework for automatically annotating the keypoints of a human body in images for learning 2D pose estimation models. Ground-truth annotations for supervised learning are difficult and cumbersome in most machine vision tasks. While considerable contributions in the community provide us a huge number of pose-annotated images, all of them mainly focus on people wearing common clothes, which are relatively easy to annotate the body keypoints. This paper, on the other hand, focuses on annotating people wearing loose-fitting clothes (e.g., Japanese Kimono) that occlude many body keypoints. In order to automatically and correctly annotate these people, we divert the 3D coordinates of the keypoints observed without loose-fitting clothes, which can be captured by a motion capture system (MoCap). These 3D keypoints are projected to an image where the body pose under loose-fitting clothes is similar to the one captured by the MoCap. Pose similarity between bodies with and without loose-fitting clothes is evaluated with 3D geometric configurations of MoCap markers that are visible even with loose-fitting clothes (e.g., markers on the head, wrists, and ankles). Experimental results validate the effectiveness of our proposed framework for human pose estimation.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019MVP0007/_p
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@ARTICLE{e103-d_6_1257,
author={Takuya MATSUMOTO, Kodai SHIMOSATO, Takahiro MAEDA, Tatsuya MURAKAMI, Koji MURAKOSO, Kazuhiko MINO, Norimichi UKITA, },
journal={IEICE TRANSACTIONS on Information},
title={Human Pose Annotation Using a Motion Capture System for Loose-Fitting Clothes},
year={2020},
volume={E103-D},
number={6},
pages={1257-1264},
abstract={This paper proposes a framework for automatically annotating the keypoints of a human body in images for learning 2D pose estimation models. Ground-truth annotations for supervised learning are difficult and cumbersome in most machine vision tasks. While considerable contributions in the community provide us a huge number of pose-annotated images, all of them mainly focus on people wearing common clothes, which are relatively easy to annotate the body keypoints. This paper, on the other hand, focuses on annotating people wearing loose-fitting clothes (e.g., Japanese Kimono) that occlude many body keypoints. In order to automatically and correctly annotate these people, we divert the 3D coordinates of the keypoints observed without loose-fitting clothes, which can be captured by a motion capture system (MoCap). These 3D keypoints are projected to an image where the body pose under loose-fitting clothes is similar to the one captured by the MoCap. Pose similarity between bodies with and without loose-fitting clothes is evaluated with 3D geometric configurations of MoCap markers that are visible even with loose-fitting clothes (e.g., markers on the head, wrists, and ankles). Experimental results validate the effectiveness of our proposed framework for human pose estimation.},
keywords={},
doi={10.1587/transinf.2019MVP0007},
ISSN={1745-1361},
month={June},}
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TY - JOUR
TI - Human Pose Annotation Using a Motion Capture System for Loose-Fitting Clothes
T2 - IEICE TRANSACTIONS on Information
SP - 1257
EP - 1264
AU - Takuya MATSUMOTO
AU - Kodai SHIMOSATO
AU - Takahiro MAEDA
AU - Tatsuya MURAKAMI
AU - Koji MURAKOSO
AU - Kazuhiko MINO
AU - Norimichi UKITA
PY - 2020
DO - 10.1587/transinf.2019MVP0007
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2020
AB - This paper proposes a framework for automatically annotating the keypoints of a human body in images for learning 2D pose estimation models. Ground-truth annotations for supervised learning are difficult and cumbersome in most machine vision tasks. While considerable contributions in the community provide us a huge number of pose-annotated images, all of them mainly focus on people wearing common clothes, which are relatively easy to annotate the body keypoints. This paper, on the other hand, focuses on annotating people wearing loose-fitting clothes (e.g., Japanese Kimono) that occlude many body keypoints. In order to automatically and correctly annotate these people, we divert the 3D coordinates of the keypoints observed without loose-fitting clothes, which can be captured by a motion capture system (MoCap). These 3D keypoints are projected to an image where the body pose under loose-fitting clothes is similar to the one captured by the MoCap. Pose similarity between bodies with and without loose-fitting clothes is evaluated with 3D geometric configurations of MoCap markers that are visible even with loose-fitting clothes (e.g., markers on the head, wrists, and ankles). Experimental results validate the effectiveness of our proposed framework for human pose estimation.
ER -