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

2017

Article

ps

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

@article{SIP,
  title = {Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs},
  author = {{von Marcard}, Timo and Rosenhahn, Bodo and Black, Michael and Pons-Moll, Gerard},
  journal = {Computer Graphics Forum 36(2), Proceedings of the 38th Annual Conference of the European Association for Computer Graphics (Eurographics)},
  pages = {349--360 },
  year = {2017},
  doi = {}
}