TETO: Tracking Events with Teacher Observation for Motion Estimation and Frame Interpolation

Event cameras capture per-pixel brightness changes with mi-crosecond resolution, offering continuous motion information lost be-tween RGB frames. However, existing event-based motion estimatorsdepend on large-scale synthetic data that often suffers from a signifi-cant sim-to-real gap. We propose TETO (Tracking Events with TeacherObservation), a teacher-student framework that learns event motionestimation from only ∼25 minutes of unannotated real-world record-ings through knowledge distillation from a pretrained RGB tracker. Ourmotion-aware data curation and query sampling strategy maximizes learn-ing from limited data by disentangling object motion from dominantego-motion. The resulting estimator jointly predicts point trajectoriesand dense optical flow, which we leverage as explicit motion priors tocondition a pretrained video diffusion transformer for frame interpola-tion. We achieve state-of-the-art point tracking on EVIMO2 and opticalflow on DSEC using orders of magnitude less training data, and demon-strate that accurate motion estimation translates directly to superiorframe interpolation quality on BS-ERGB and HQ-EVFI.