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Deep inertial poser: learning to reconstruct human pose from sparse inertial measurements in real time
Open accessAbstract
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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“Recent approaches addressing the lack of annotated datasets involve extraction of virtual movement data from modalities such as mocap [37, 72] and videos [44], which contain a large number of participants and diverse set of actions and environments.”
From Contrastive Predictive Coding for Human Activity Recognition (2021) · cites this paper as [37]
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Metadata
- 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
- Venue
- ACM Transactions on Graphics
- Published
- 2018-12-04
- Volume
- 37
- Issue
- 6
- Pages
- 1-15
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- 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
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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