  We introduce (HPS) Human POSEitioning System, a method to recover the full 3D
pose of a human registered with a 3D scan of the surrounding environment using
wearable sensors. Using IMUs attached at the body limbs and a head mounted
camera looking outwards, HPS fuses camera based self-localization with
IMU-based human body tracking. The former provides drift-free but noisy
position and orientation estimates while the latter is accurate in the
short-term but subject to drift over longer periods of time. We show that our
optimization-based integration exploits the benefits of the two, resulting in
pose accuracy free of drift. Furthermore, we integrate 3D scene constraints
into our optimization, such as foot contact with the ground, resulting in
physically plausible motion. HPS complements more common third-person-based 3D
pose estimation methods. It allows capturing larger recording volumes and
longer periods of motion, and could be used for VR/AR applications where humans
interact with the scene without requiring direct line of sight with an external
camera, or to train agents that navigate and interact with the environment
based on first-person visual input, like real humans. With HPS, we recorded a
dataset of humans interacting with large 3D scenes (300-1000 sq.m) consisting
of 7 subjects and more than 3 hours of diverse motion. The dataset, code and
video will be available on the project page:
http://virtualhumans.mpi-inf.mpg.de/hps/ .
