  This paper tackles the problem of passive gaze estimation using both event
and frame data. Considering the inherently different physiological structures,
it is intractable to accurately estimate gaze purely based on a given state.
Thus, we reformulate gaze estimation as the quantification of the state
shifting from the current state to several prior registered anchor states.
Specifically, we propose a two-stage learning-based gaze estimation framework
that divides the whole gaze estimation process into a coarse-to-fine approach
involving anchor state selection and final gaze location. Moreover, to improve
the generalization ability, instead of learning a large gaze estimation network
directly, we align a group of local experts with a student network, where a
novel denoising distillation algorithm is introduced to utilize denoising
diffusion techniques to iteratively remove inherent noise in event data.
Extensive experiments demonstrate the effectiveness of the proposed method,
which surpasses state-of-the-art methods by a large margin of 15$\%$. The code
will be publicly available at
https://github.com/jdjdli/Denoise_distill_EF_gazetracker.
