Overcoming Data Scarcity for Event-Based Pupil Tracking with Synthetic and Unlabeled Data ETRA018

Smart eyewear is emerging as an always-on platform capable of perceiving the environment and inferring intent through eye movements, making pupil tracking essential for personalized interaction. However, reliable tracking on wearable hardware remains challenging due to strict power limits and scarce annotated data. Event-based cameras offer a low-power, microsecond-latency solution, but labeled recordings still remain limited. We address this issue with a training framework that combines limited annotated real data with synthetic events and unlabeled real recordings, learning event-based pupil trackers with strong real-world generalization. We pair the U2Eyes tool with the v2e event camera simulator to generate realistic event streams, showing that networks trained on these events exhibit smaller sim-to-real gaps than networks trained on synthetic images. Moreover, our training procedure further bridges this gap, enabling our models to outperform networks trained exclusively on real data across all benchmarks, advancing toward more robust event-based eye tracking on wearable platforms.