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dc.contributor.authorMarcard, Timo vonen_US
dc.contributor.authorRosenhahn, Bodoen_US
dc.contributor.authorBlack, Michael J.en_US
dc.contributor.authorPons-Moll, Gerarden_US
dc.contributor.editorLoic Barthe and Bedrich Benesen_US
dc.date.accessioned2017-04-22T16:26:54Z
dc.date.available2017-04-22T16:26:54Z
dc.date.issued2017
dc.identifier.issn1467-8659
dc.identifier.urihttp://dx.doi.org/10.1111/cgf.13131
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13131
dc.description.abstractWe 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.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectI.3.3 [Computer Graphics]
dc.subjectThree Dimensional Graphics and Realism
dc.subjectAnimation
dc.titleSparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUsen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersAnimation 2
dc.description.volume36
dc.description.number2
dc.identifier.doi10.1111/cgf.13131
dc.identifier.pages349-360


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