Data-Driven Reconstruction of Human Locomotion Using a Single Smartphone
Generating a visually appealing human motion sequence using low‐dimensional control signals is a major line of study in the motion research area in computer graphics. We propose a novel approach that allows us to reconstruct full body human locomotion using a single inertial sensing device, a smartp...
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| Published in | Computer graphics forum Vol. 33; no. 7; pp. 11 - 19 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.10.2014
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0167-7055 1467-8659 |
| DOI | 10.1111/cgf.12469 |
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| Abstract | Generating a visually appealing human motion sequence using low‐dimensional control signals is a major line of study in the motion research area in computer graphics. We propose a novel approach that allows us to reconstruct full body human locomotion using a single inertial sensing device, a smartphone. Smartphones are among the most widely used devices and incorporate inertial sensors such as an accelerometer and a gyroscope. To find a mapping between a full body pose and smartphone sensor data, we perform low dimensional embedding of full body motion capture data, based on a Gaussian Process Latent Variable Model. Our system ensures temporal coherence between the reconstructed poses by using a state decomposition model for automatic phase segmentation. Finally, application of the proposed nonlinear regression algorithm finds a proper mapping between the latent space and the sensor data. Our framework effectively reconstructs plausible 3D locomotion sequences. We compare the generated animation to ground truth data obtained using a commercial motion capture system. |
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| AbstractList | Generating a visually appealing human motion sequence using low-dimensional control signals is a major line of study in the motion research area in computer graphics. We propose a novel approach that allows us to reconstruct full body human locomotion using a single inertial sensing device, a smartphone. Smartphones are among the most widely used devices and incorporate inertial sensors such as an accelerometer and a gyroscope. To find a mapping between a full body pose and smartphone sensor data, we perform low dimensional embedding of full body motion capture data, based on a Gaussian Process Latent Variable Model. Our system ensures temporal coherence between the reconstructed poses by using a state decomposition model for automatic phase segmentation. Finally, application of the proposed nonlinear regression algorithm finds a proper mapping between the latent space and the sensor data. Our framework effectively reconstructs plausible 3D locomotion sequences. We compare the generated animation to ground truth data obtained using a commercial motion capture system. |
| Author | Noh, Junyong Choi, Byungkuk Eom, Haegwang |
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| Copyright | 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2014 The Eurographics Association and John Wiley & Sons Ltd. |
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| References_xml | – reference: Semwal S.K., Hightower R., Stansfield S.: Mapping algorithms for real-time control of an avatar using eight sensors. Presence: Teleoperators and Virtual Environments 7, 1 (1998), 1-21. 2 – reference: Oore S., Terzopoulos D., Hinton G.: A desktop input device and interface for interactive 3d character animation. In Graphics Interface (2002), vol. 2, pp. 133-140. 2 – reference: Shiratori T., Hodgins J.K.: Accelerometer-based user interfaces for the control of a physically simulated character. In ACM Transactions on Graphics (TOG) (2008), vol. 27, ACM, p. 123. 2 – reference: Shoemake K.: Animating rotation with quaternion curves. ACM SIGGRAPH computer graphics 19, 3 (1985), 245-254. 6 – reference: Wang J.M., Fleet D.J., Hertzmann A.: Gaussian process dynamical models for human motion. Pattern Analysis and Machine Intelligence, IEEE Transactions on 30, 2 (2008), 283-298. 2, 5 – reference: Min J., Chen Y.-L., Chai J.: Interactive generation of human animation with deformable motion models. ACM Transactions on Graphics (TOG) 29, 1 (2009), 9. 5 – reference: Levine S., Wang J.M., Haraux A., Popović Z., Koltun V.: Continuous character control with low-dimensional embeddings. ACM Transactions on Graphics (TOG) 31, 4 (2012), 28. 2, 3, 4 – reference: Lawrence N.: Probabilistic non-linear principal component analysis with gaussian process latent variable models. The Journal of Machine Learning Research 6 (2005), 1783-1816. 3 – reference: Alpaydin E.: Introduction to machine learning. MIT press, 2004, pp. 305-326. 5 – reference: Grochow K., Martin S.L., Hertzmann A., Popović Z.: Style-based inverse kinematics. In ACM Transactions on Graphics (TOG) (2004), vol. 23, ACM, pp. 522-531. 2, 3 – reference: Lawrence N.D.: Gaussian process latent variable models for visualisation of high dimensional data. Advances in neural information processing systems 16, 329-336 (2004), 3. 2 – reference: Tautges J., Zinke A., Krüger B., Baumann J., Weber A., Helten T., Müller M., Seidel H.-P., Eberhardt B.: Motion reconstruction using sparse accelerometer data. ACM Transactions on Graphics (TOG) 30, 3 (2011), 18. 2, 7 – reference: Dontcheva M., Yngve G., Popović Z.: Layered acting for character animation. ACM Transactions on Graphics (TOG) 22, 3 (2003), 409-416. 2 – reference: Tournier M., Wu X., Courty N., Arnaud E., Reveret L.: Motion compression using principal geodesics analysis. In Computer Graphics Forum (2009), vol. 28, Wiley Online Library, pp. 355-364. 7 – reference: Curtin R.R., Cline J.R., Slagle N.P., March W.B., Ram P., Mehta N.A., Gray A.G.: MLPACK: A scalable C++ machine learning library. Journal of Machine Learning Research 14 (2013), 801-805. 7 – reference: Kim M., Hyun K., Kim J., Lee J.: Synchronized multi-character motion editing. In ACM Transactions on Graphics (TOG) (2009), vol. 28, ACM, p. 79. 6 – reference: Chai J., Hodgins J.K.: Performance animation from low-dimensional control signals. In ACM Transactions on Graphics (TOG) (2005), vol. 24, ACM, pp. 686-696. 2, 8 – volume: 31 start-page: 28 year: 2012 article-title: Continuous character control with low‐dimensional embeddings publication-title: ACM Transactions on Graphics (TOG) – start-page: 193 year: 2008 end-page: 199 – volume: 28 start-page: 79 year: 2009 article-title: Synchronized multi‐character motion editing publication-title: ACM Transactions on Graphics (TOG) – volume: 2 start-page: 133 year: 2002 end-page: 140 article-title: A desktop input device and interface for interactive 3d character animation publication-title: Graphics Interface – volume: 27 start-page: 123 year: 2008 article-title: Accelerometer‐based user interfaces for the control of a physically simulated character publication-title: ACM Transactions on Graphics (TOG) – volume: 14 start-page: 801 year: 2013 end-page: 805 article-title: MLPACK: A scalable C++ machine learning library publication-title: Journal of Machine Learning Research – 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doi: 10.1145/2185520.2185524 – ident: e_1_2_10_22_2 – volume: 6 start-page: 1783 year: 2005 ident: e_1_2_10_13_2 article-title: Probabilistic non‐linear principal component analysis with gaussian process latent variable models publication-title: The Journal of Machine Learning Research – ident: e_1_2_10_3_2 – ident: e_1_2_10_9_2 doi: 10.1145/1015706.1015755 – ident: e_1_2_10_19_2 – ident: e_1_2_10_26_2 doi: 10.1145/1966394.1966397 – ident: e_1_2_10_25_2 doi: 10.1111/j.1467-8659.2009.01375.x – volume: 14 start-page: 801 year: 2013 ident: e_1_2_10_5_2 article-title: MLPACK: A scalable C++ machine learning library publication-title: Journal of Machine Learning Research – volume: 2 start-page: 133 year: 2002 ident: e_1_2_10_16_2 article-title: A desktop input device and interface for interactive 3d character animation publication-title: Graphics Interface – ident: e_1_2_10_28_2 doi: 10.1145/1536513.1536551 – ident: e_1_2_10_8_2 doi: 10.1109/AFGR.1996.557250 – volume: 16 start-page: 329 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| SubjectTerms | Analysis Categories and Subject Descriptors (according to ACM CCS) Computer graphics Human body I.3.7 [Three-Dimensional Graphics and Realism]: Animation Motion capture Movement Sensors Smartphones Studies |
| Title | Data-Driven Reconstruction of Human Locomotion Using a Single Smartphone |
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