Sparse Inertial Poser: Automatic 3D Human Pose Estimation from Sparse IMUs
arXiv:1703.08014
Abstract
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 3D human pose estimation 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.
12 pages, Accepted at Eurographics 2017
Cited by in corpus (6)
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- Learning to Estimate 3D Human Pose and Shape from a Single Color Image
- Mo2Cap2: Real-time Mobile 3D Motion Capture with a Cap-mounted Fisheye Camera
- Fusing Wearable IMUs with Multi-View Images for Human Pose Estimation: A Geometric Approach
- DeepFuse: An IMU-Aware Network for Real-Time 3D Human Pose Estimation from Multi-View Image
- RIDI: Robust IMU Double Integration