  Event-based cameras are becoming increasingly popular for their ability to
capture high-speed motion with low latency and high dynamic range. However,
generating videos from events remains challenging due to the highly sparse and
varying nature of event data. To address this, in this study, we propose
HyperE2VID, a dynamic neural network architecture for event-based video
reconstruction. Our approach uses hypernetworks to generate per-pixel adaptive
filters guided by a context fusion module that combines information from event
voxel grids and previously reconstructed intensity images. We also employ a
curriculum learning strategy to train the network more robustly. Our
comprehensive experimental evaluations across various benchmark datasets reveal
that HyperE2VID not only surpasses current state-of-the-art methods in terms of
reconstruction quality but also achieves this with fewer parameters, reduced
computational requirements, and accelerated inference times.
