  We propose a hardware and software pipeline to fabricate flexible wearable
sensors and use them to capture deformations without line of sight. Our first
contribution is a low-cost fabrication pipeline to embed multiple aligned
conductive layers with complex geometries into silicone compounds. Overlapping
conductive areas from separate layers form local capacitors that measure dense
area changes. Contrary to existing fabrication methods, the proposed technique
only requires hardware that is readily available in modern fablabs. While area
measurements alone are not enough to reconstruct the full 3D deformation of a
surface, they become sufficient when paired with a data-driven prior. A novel
semi-automatic tracking algorithm, based on an elastic surface geometry
deformation, allows to capture ground-truth data with an optical mocap system,
even under heavy occlusions or partially unobservable markers. The resulting
dataset is used to train a regressor based on deep neural networks, directly
mapping the area readings to global positions of surface vertices. We
demonstrate the flexibility and accuracy of the proposed hardware and software
in a series of controlled experiments, and design a prototype of wearable
wrist, elbow and biceps sensors, which do not require line-of-sight and can be
worn below regular clothing.
