DiffEyeSyn: Diffusion-based User-specific Eye Movement Synthesis

Abstract: High-frequency gaze data contains more user-specific information than low-frequency data, promising for various applications. However, existing gaze modelling methods focus on low-frequency data, ignoring user-specific subtle eye movements in high-frequency eye movements. We present DiffEyeSyn -- the first computational method to synthesise eye movements specific to individual users. The key idea is to consider the user-specific information as a special type of noise in eye movement data. This perspective reshapes eye movement synthesis into the task of injecting this user-specific noise into any given eye movement sequence. We formulate this injection task as a conditional diffusion process in which the synthesis is conditioned on user-specific embeddings extracted from the gaze data using pre-trained models for user authentication. We propose user identity guidance -- a novel loss function that allows our model to preserve user identity while generating human-like eye movements in the spatial domain. Experiments on two public datasets show that our synthetic eye movements preserve user-specific characteristics and are more realistic than baseline approaches. Furthermore, we demonstrate that DiffEyeSyn can synthesise large-scale gaze data and support various downstream tasks, such as gaze-based user identification. As such, our work lays the methodological foundations for personalised eye movement synthesis that has significant application potential, such as for character animation, eye movement biometrics, and gaze data imputation.