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dc.contributor.authorEom, Haegwangen_US
dc.contributor.authorChoi, Byungkuken_US
dc.contributor.authorNoh, Junyongen_US
dc.contributor.editorJ. Keyser, Y. J. Kim, and P. Wonkaen_US
dc.description.abstractGenerating 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.en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleData-Driven Reconstruction of Human Locomotion Using a Single Smartphoneen_US
dc.description.seriesinformationComputer Graphics Forumen_US

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  • 33-Issue 7
    Pacific Graphics 2014 - Special Issue

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