  Event cameras are biologically-inspired sensors that gather the temporal
evolution of the scene. They capture pixel-wise brightness variations and
output a corresponding stream of asynchronous events. Despite having multiple
advantages with respect to traditional cameras, their use is partially
prevented by the limited applicability of traditional data processing and
vision algorithms. To this aim, we present a framework which exploits the
output stream of event cameras to synthesize RGB frames, relying on an initial
or a periodic set of color key-frames and the sequence of intermediate events.
Differently from existing work, we propose a deep learning-based frame
synthesis method, consisting of an adversarial architecture combined with a
recurrent module. Qualitative results and quantitative per-pixel, perceptual,
and semantic evaluation on four public datasets confirm the quality of the
synthesized images.
