  Event cameras provide a number of benefits over traditional cameras, such as
the ability to track incredibly fast motions, high dynamic range, and low power
consumption. However, their application into computer vision problems, many of
which are primarily dominated by deep learning solutions, has been limited by
the lack of labeled training data for events. In this work, we propose a method
which leverages the existing labeled data for images by simulating events from
a pair of temporal image frames, using a convolutional neural network. We train
this network on pairs of images and events, using an adversarial discriminator
loss and a pair of cycle consistency losses. The cycle consistency losses
utilize a pair of pre-trained self-supervised networks which perform optical
flow estimation and image reconstruction from events, and constrain our network
to generate events which result in accurate outputs from both of these
networks. Trained fully end to end, our network learns a generative model for
events from images without the need for accurate modeling of the motion in the
scene, exhibited by modeling based methods, while also implicitly modeling
event noise. Using this simulator, we train a pair of downstream networks on
object detection and 2D human pose estimation from events, using simulated data
from large scale image datasets, and demonstrate the networks' abilities to
generalize to datasets with real events.
