Multi-task and multi-level detection neural network based real-time 3D pose estimation

Dingli Luo, Songlin Du, Takeshi Ikenaga

研究成果: Conference contribution

抄録

3D pose estimation is a core step for human-computer interaction and human action recognition. However, time-sensitive applications like virtual reality also need this task to achieve real-time speed. This paper proposes a multitask and multi-level neural network architecture with a highspeed friendly 3D human pose representation. Based on this, we build a real-time multi-person 3D pose estimation system with a single RGB image as input. The network estimates 3D poses from the input image directly by the multi-task design and keeps both accuracy and speed by the multi-level detection design. By evaluation, we show our system achieves the 21 fps on RTX 2080 with only 33 mm accuracy lose compared with related works. We also provide network visualization to prove our network work as we design. This work shows the possibility for a single RGB image based 3D pose estimation system to achieve real-time speed, which is a basement for building a low-cost 3D motion capture system.

本文言語English
ホスト出版物のタイトル2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1427-1434
ページ数8
ISBN(電子版)9781728132488
DOI
出版ステータスPublished - 2019 11
イベント2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 - Lanzhou, China
継続期間: 2019 11 182019 11 21

出版物シリーズ

名前2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019

Conference

Conference2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019
国/地域China
CityLanzhou
Period19/11/1819/11/21

ASJC Scopus subject areas

  • 情報システム

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