  For facial motion capture and analysis, the dominated solutions are generally
based on visual cues, which cannot protect privacy and are vulnerable to
occlusions. Inertial measurement units (IMUs) serve as potential rescues yet
are mainly adopted for full-body motion capture. In this paper, we propose
IMUSE to fill the gap, a novel path for facial expression capture using purely
IMU signals, significantly distant from previous visual solutions.The key
design in our IMUSE is a trilogy. We first design micro-IMUs to suit facial
capture, companion with an anatomy-driven IMU placement scheme. Then, we
contribute a novel IMU-ARKit dataset, which provides rich paired IMU/visual
signals for diverse facial expressions and performances. Such unique
multi-modality brings huge potential for future directions like IMU-based
facial behavior analysis. Moreover, utilizing IMU-ARKit, we introduce a strong
baseline approach to accurately predict facial blendshape parameters from
purely IMU signals. The IMUSE framework empowers us to perform accurate facial
capture in scenarios where visual methods falter and simultaneously safeguard
user privacy. We conduct extensive experiments about both the IMU configuration
and technical components to validate the effectiveness of our IMUSE approach.
Notably, IMUSE enables various potential and novel applications, i.e., facial
capture against occlusions or in a moving performance. We will release our
dataset and implementations to enrich more possibilities of facial capture and
analysis in our community.
