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Event cameras have the ability to record continuous and detailed trajectoriesof objects with high temporal resolution, thereby providing intuitive motioncues for optical flow estimation. Nevertheless, most existing learning-basedapproaches for event optical flow estimation directly remould the paradigm ofconventional images by representing the consecutive event stream as staticframes, ignoring the inherent temporal continuity of event data. In this paper,we argue that temporal continuity is a vital element of event-based opticalflow and propose a novel Temporal Motion Aggregation (TMA) approach to unlockits potential. Technically, TMA comprises three components: an event splittingstrategy to incorporate intermediate motion information underlying the temporalcontext, a linear lookup strategy to align temporally fine-grained motionfeatures and a novel motion pattern aggregation module to emphasize consistentpatterns for motion feature enhancement. By incorporating temporallyfine-grained motion information, TMA can derive better flow estimates thanexisting methods at early stages, which not only enables TMA to obtain moreaccurate final predictions, but also greatly reduces the demand for a number ofrefinements. Extensive experiments on DSEC-Flow and MVSEC datasets verify theeffectiveness and superiority of our TMA. Remarkably, compared to E-RAFT, TMAachieves a 6\% improvement in accuracy and a 40\% reduction in inference timeon DSEC-Flow. Code will be available at \url{https://github.com/ispc-lab/TMA}.

TMA: Temporal Motion Aggregation for Event-based Optical Flow