Diffusion Versus GAN for Subject-Specific Gaze Synthesis

Generating realistic, subject-specific eye movement signals is crucial for data augmentation, gaze-based authentication, privacy-preserving analytics, and robust gaze-based interfaces. Recent advances in deep learning have enabled the generation of synthetic gaze data, but individualized gaze sequence generation has been less explored, with most approaches relying on random noise distributions or predefined latent embeddings. In this work, our main contribution is the introduction of explicit subject-aware modifications to both diffusion and generative adversarial networks (GANs)-based methods for generating realistic, individualized synthetic gaze data. In particular, we equip the diffusion-based method with compact user embeddings to capture per-subject traits and enhance the GAN-based generator with a subject-specific synthesis module to better retain idiosyncratic gaze information. We comprehensively assess these subject-aware methods with standard eye-tracking signal quality metrics, including spatial accuracy and precision. This work defines synthetic signal quality, realism, and subject specificity, and advances the potential of subject-aware gaze-based applications.