  Visual reconstruction of fast non-rigid object deformations over time is a
challenge for conventional frame-based cameras. In this paper, we propose a
novel approach for reconstructing such deformations using measurements from
event-based cameras. Under the assumption of a static background, where all
events are generated by the motion, our approach estimates the deformation of
objects from events generated at the object contour in a probabilistic
optimization framework. It associates events to mesh faces on the contour and
maximizes the alignment of the line of sight through the event pixel with the
associated face. In experiments on synthetic and real data, we demonstrate the
advantages of our method over state-of-the-art optimization and learning-based
approaches for reconstructing the motion of human hands. A video of the
experiments is available at https://youtu.be/gzfw7i5OKjg
