The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs
Open accessAbstract
We address the problem of making human motion capture in the wild more practical by using a small set of inertial sensors attached to the body. Since the problem is heavily under-constrained, previous methods either use a large number of sensors, which is intrusive, or they require additional video input. We take a different approach and constrain the problem by: (i) making use of a realistic statistical body model that includes anthropometric constraints and (ii) using a joint optimization framework to fit the model to orientation and acceleration measurements over multiple frames. The resulting tracker Sparse Inertial Poser (SIP) enables motion capture using only 6 sensors (attached to the wrists, lower legs, back and head) and works for arbitrary human motions. Experiments on the recently released TNT15 dataset show that, using the same number of sensors, SIP achieves higher accuracy than the dataset baseline without using any video data. We further demonstrate the effectiveness of SIP on newly recorded challenging motions in outdoor scenarios such as climbing or jumping over a wall.
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Self-citationHuman3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
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Self-citationSMPL: A Skinned Multi-Person Linear Model
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Self-citationArticulated mesh animation from multi-view silhouettes
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Pavlakos, G., Zhu, L., Zhou, X., & Daniilidis, K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00055 · Cited by 607
Huang, Y., Kaufmann, M., Aksan, E., Black, M. J., Hilliges, O., & Pons-Moll, G. (2018) · ACM Transactions on Graphics · doi:10.1145/3272127.3275108 · Cited by 342
Self-citationSTAR: Sparse Trained Articulated Human Body Regressor
Osman, A. A. A., Bolkart, T., & Black, M. J. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58539-6_36 · Cited by 252
Self-citationA Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives
Mathis, A., Schneider, S., Lauer, J., & Mathis, M. W. (2020) · Neuron · doi:10.1016/j.neuron.2020.09.017 · Cited by 231
Deep Neural Network-Based Cooperative Visual Tracking Through Multiple Micro Aerial Vehicles
Price, E., Lawless, G., Ludwig, R., Martinovic, I., Bulthoff, H. H., Black, M. J., & Ahmad, A. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2018.2850224 · Cited by 64
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Metadata
- Authors
- von Marcard, T., Rosenhahn, B., Black, M. J., & Pons-Moll, G.
- Venue
- Computer Graphics Forum
- Publisher
- Wiley
- Published
- 2017-05
- Volume
- 36
- Issue
- 2
- Pages
- 349-360
- ISSN
- 0167-7055 (ISSN-L)
- OpenAlex
- W2605243700
- Cited by
- 304
- References
- 29
- FWCI
- 17.39
- Type
- journal article
- Language
- en
- Open access
- green
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- von Marcard, T., Rosenhahn, B., Black, M. J., & Pons-Moll, G. (2017). Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs. Computer Graphics Forum. https://doi.org/10.1111/cgf.13131
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