Cited by 342Human Pose and Action RecognitionHuman Motion and Animation3D Shape Modeling and Analysis

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Deep inertial poser: learning to reconstruct human pose from sparse inertial measurements in real time

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Huang, Yinghao, Kaufmann, Manuel, Aksan, Emre, Black, Michael J., Hilliges, Otmar, Pons-Moll, Gerard·2018·ACM Transactions on Graphics

DOI 10.1145/3272127.3275108

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Abstract

We demonstrate a novel deep neural network capable of reconstructing human full body pose in real-time from 6 Inertial Measurement Units (IMUs) worn on the user's body. In doing so, we address several difficult challenges. First, the problem is severely under-constrained as multiple pose parameters produce the same IMU orientations. Second, capturing IMU data in conjunction with ground-truth poses is expensive and difficult to do in many target application scenarios (e.g., outdoors). Third, modeling temporal dependencies through non-linear optimization has proven effective in prior work but makes real-time prediction infeasible. To address this important limitation, we learn the temporal pose priors using deep learning. To learn from sufficient data, we synthesize IMU data from motion capture datasets. A bi-directional RNN architecture leverages past and future information that is available at training time. At test time, we deploy the network in a sliding window fashion, retaining real time capabilities. To evaluate our method, we recorded DIP-IMU, a dataset consisting of 10 subjects wearing 17 IMUs for validation in 64 sequences with 330 000 time instants; this constitutes the largest IMU dataset publicly available. We quantitatively evaluate our approach on multiple datasets and show results from a real-time implementation. DIP-IMU and the code are available for research purposes. 1

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Authors: Huang, Y., Kaufmann, M., Aksan, E., Black, M. J., Hilliges, O., & Pons-Moll, G.
Affiliations: Max Planck Institute for Intelligent Systems (DE)
ETH Zurich (CH)
Max Planck Institute for Informatics, Saarbrücken, Germany
ORCID: Huang, Yinghao
Kaufmann, Manuel
Aksan, Emre
Black, Michael J.
Hilliges, Otmar
Venue: ACM Transactions on Graphics
Publisher: Association for Computing Machinery (ACM)
Published: 2018-12-04
Volume: 37
Issue: 6
Pages: 1-15
ISSN: 0730-0301 (ISSN-L)
1557-7368
DOI: 10.1145/3272127.3275108
OpenAlex: W2898073175
Cited by: 342
References: 72
Citations by year: 2026: 43 · 2025: 54 · 2024: 69 · 2023: 56 · 2022: 38 · 2021: 44 · 2020: 26 · 2019: 12
FWCI: 10.25
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: ACM Transactions on Graphics (publishedVersion)
Funders: Nvidia
H2020 European Research Council (717054)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Human Motion and Animation
Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
Keywords: inertial measurement unit, full-body pose estimation, real-time pose estimation, bidirectional recurrent neural network, motion capture, wearable sensors

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Deep inertial poser: learning to reconstruct human pose from sparse inertial measurements in real time

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