  Real-time human motion reconstruction from a sparse set of (e.g. six)
wearable IMUs provides a non-intrusive and economic approach to motion capture.
Without the ability to acquire position information directly from IMUs, recent
works took data-driven approaches that utilize large human motion datasets to
tackle this under-determined problem. Still, challenges remain such as temporal
consistency, drifting of global and joint motions, and diverse coverage of
motion types on various terrains. We propose a novel method to simultaneously
estimate full-body motion and generate plausible visited terrain from only six
IMU sensors in real-time. Our method incorporates 1. a conditional Transformer
decoder model giving consistent predictions by explicitly reasoning prediction
history, 2. a simple yet general learning target named "stationary body points"
(SBPs) which can be stably predicted by the Transformer model and utilized by
analytical routines to correct joint and global drifting, and 3. an algorithm
to generate regularized terrain height maps from noisy SBP predictions which
can in turn correct noisy global motion estimation. We evaluate our framework
extensively on synthesized and real IMU data, and with real-time live demos,
and show superior performance over strong baseline methods.
