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We address the problem of making human motion capture in the wild morepractical by using a small set of inertial sensors attached to the body. Sincethe problem is heavily under-constrained, previous methods either use a largenumber 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 arealistic statistical body model that includes anthropometric constraints and(ii) using a joint optimization framework to fit the model to orientation andacceleration measurements over multiple frames. The resulting tracker SparseInertial Poser (SIP) enables 3D human pose estimation using only 6 sensors(attached to the wrists, lower legs, back and head) and works for arbitraryhuman motions. Experiments on the recently released TNT15 dataset show that,using the same number of sensors, SIP achieves higher accuracy than the datasetbaseline without using any video data. We further demonstrate the effectivenessof SIP on newly recorded challenging motions in outdoor scenarios such asclimbing or jumping over a wall.

Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs