  Event cameras have a lot of advantages over traditional cameras, such as low
latency, high temporal resolution, and high dynamic range. However, since the
outputs of event cameras are the sequences of asynchronous events overtime
rather than actual intensity images, existing algorithms could not be directly
applied. Therefore, it is demanding to generate intensity images from events
for other tasks. In this paper, we unlock the potential of event camera-based
conditional generative adversarial networks to create images/videos from an
adjustable portion of the event data stream. The stacks of space-time
coordinates of events are used as inputs and the network is trained to
reproduce images based on the spatio-temporal intensity changes. The usefulness
of event cameras to generate high dynamic range(HDR) images even in extreme
illumination conditions and also non blurred images under rapid motion is also
shown.In addition, the possibility of generating very high frame rate videos is
demonstrated, theoretically up to 1 million frames per second (FPS) since the
temporal resolution of event cameras are about 1{\mu}s. Proposed methods are
evaluated by comparing the results with the intensity images captured on the
same pixel grid-line of events using online available real datasets and
synthetic datasets produced by the event camera simulator.
