  Current optical flow and point-tracking methods rely heavily on synthetic
datasets. Event cameras are novel vision sensors with advantages in challenging
visual conditions, but state-of-the-art frame-based methods cannot be easily
adapted to event data due to the limitations of current event simulators. We
introduce a novel self-supervised loss combining the Contrast Maximization
framework with a non-linear motion prior in the form of pixel-level
trajectories and propose an efficient solution to solve the high-dimensional
assignment problem between non-linear trajectories and events. Their
effectiveness is demonstrated in two scenarios: In dense continuous-time motion
estimation, our method improves the zero-shot performance of a synthetically
trained model on the real-world dataset EVIMO2 by 29%. In optical flow
estimation, our method elevates a simple UNet to achieve state-of-the-art
performance among self-supervised methods on the DSEC optical flow benchmark.
Our code is available at https://github.com/tub-rip/MotionPriorCMax.
