  Event cameras provide high temporal precision, low data rates, and high
dynamic range visual perception, which are well-suited for optical flow
estimation. While data-driven optical flow estimation has obtained great
success in RGB cameras, its generalization performance is seriously hindered in
event cameras mainly due to the limited and biased training data. In this
paper, we present a novel simulator, BlinkSim, for the fast generation of
large-scale data for event-based optical flow. BlinkSim consists of a
configurable rendering engine and a flexible engine for event data simulation.
By leveraging the wealth of current 3D assets, the rendering engine enables us
to automatically build up thousands of scenes with different objects, textures,
and motion patterns and render very high-frequency images for realistic event
data simulation. Based on BlinkSim, we construct a large training dataset and
evaluation benchmark BlinkFlow that contains sufficient, diversiform, and
challenging event data with optical flow ground truth. Experiments show that
BlinkFlow improves the generalization performance of state-of-the-art methods
by more than 40% on average and up to 90%. Moreover, we further propose an
Event optical Flow transFormer (E-FlowFormer) architecture. Powered by our
BlinkFlow, E-FlowFormer outperforms the SOTA methods by up to 91% on MVSEC
dataset and 14% on DSEC dataset and presents the best generalization
performance.
