AI RESEARCH
TETO: Tracking Events with Teacher Observation for Motion Estimation and Frame Interpolation
arXiv CS.CV
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ArXi:2603.23487v1 Announce Type: new Event cameras capture per-pixel brightness changes with microsecond resolution, offering continuous motion information lost between RGB frames. However, existing event-based motion estimators depend on large-scale synthetic data that often suffers from a significant sim-to-real gap. We propose TETO (Tracking Events with Teacher Observation), a teacher-student framework that learns event motion estimation from only $\sim$25 minutes of unannotated real-world recordings through knowledge distillation from a pretrained RGB tracker.