  Event cameras have the ability to record continuous and detailed trajectories
of objects with high temporal resolution, thereby providing intuitive motion
cues for optical flow estimation. Nevertheless, most existing learning-based
approaches for event optical flow estimation directly remould the paradigm of
conventional images by representing the consecutive event stream as static
frames, ignoring the inherent temporal continuity of event data. In this paper,
we argue that temporal continuity is a vital element of event-based optical
flow and propose a novel Temporal Motion Aggregation (TMA) approach to unlock
its potential. Technically, TMA comprises three components: an event splitting
strategy to incorporate intermediate motion information underlying the temporal
context, a linear lookup strategy to align temporally fine-grained motion
features and a novel motion pattern aggregation module to emphasize consistent
patterns for motion feature enhancement. By incorporating temporally
fine-grained motion information, TMA can derive better flow estimates than
existing methods at early stages, which not only enables TMA to obtain more
accurate final predictions, but also greatly reduces the demand for a number of
refinements. Extensive experiments on DSEC-Flow and MVSEC datasets verify the
effectiveness and superiority of our TMA. Remarkably, compared to E-RAFT, TMA
achieves a 6\% improvement in accuracy and a 40\% reduction in inference time
on DSEC-Flow. Code will be available at \url{https://github.com/ispc-lab/TMA}.
