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Human Pose Annotation Using a Motion Capture System for Loose-Fitting Clothes

Takuya MATSUMOTO, Kodai SHIMOSATO, Takahiro MAEDA, Tatsuya MURAKAMI, Koji MURAKOSO, Kazuhiko MINO, Norimichi UKITA

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Summary :

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.

Publication
IEICE TRANSACTIONS on Information Vol.E103-D No.6 pp.1257-1264
Publication Date
2020/06/01
Publicized
2020/03/30
Online ISSN
1745-1361
DOI
10.1587/transinf.2019MVP0007
Type of Manuscript
Special Section PAPER (Special Section on Machine Vision and its Applications)
Category

Authors

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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