Deformation Capture via Self-Sensing Capacitive Arrays (Video)

In this video we present soft self-sensing capacitive arrays and demonstrate their use in capturing dense surface deformations without requiring line of sight. The capacitive arrays are made of two electrode patterns embedded into a single silicone compound. The overlaps of the electrode strip patterns form local capacitors. As the sensor is stretched the local capacitance measurements change. We introduce a fabrication technique that allows to produce such sensors while only requiring hardware readily available in modern fablabs. The resulting sensors are able to densely capture area changes as they deform. Since they do not directly measure bend, a prior is required to fully reconstruct the underlying motion. We propose a deep neural network regressing the sensor geometry from the local area measurements. A motion capture system is used for training data acquisition. At runtime vertex positions are predicted and used as positional constraints to deform a mesh using a state-of-the-art elastic surface energy. The flexibility and accuracy of the introduced sensors is demonstrated in a series of controlled experiments and by fabricating a prototype sensor and applying it to capture deforming skeletal and non-skeletal objects.