  Event cameras are novel sensors that output brightness changes in the form of
a stream of asynchronous "events" instead of intensity frames. They offer
significant advantages with respect to conventional cameras: high dynamic range
(HDR), high temporal resolution, and no motion blur. Recently, novel learning
approaches operating on event data have achieved impressive results. Yet, these
methods require a large amount of event data for training, which is hardly
available due the novelty of event sensors in computer vision research. In this
paper, we present a method that addresses these needs by converting any
existing video dataset recorded with conventional cameras to synthetic event
data. This unlocks the use of a virtually unlimited number of existing video
datasets for training networks designed for real event data. We evaluate our
method on two relevant vision tasks, i.e., object recognition and semantic
segmentation, and show that models trained on synthetic events have several
benefits: (i) they generalize well to real event data, even in scenarios where
standard-camera images are blurry or overexposed, by inheriting the outstanding
properties of event cameras; (ii) they can be used for fine-tuning on real data
to improve over state-of-the-art for both classification and semantic
segmentation.
