arXiv:2610.30032v1 Announce Type: new Abstract: Eye-movement authentication in virtual reality (VR) needs longitudinal training data, yet most such datasets come from lab trackers. We convert lab recordings to a VR headset's signal quality while keeping their eye movement, using only unpaired VR recordings of other people. Measurements show that the headset's added noise dominates above 7.8 Hz and that its quality is consistent within a person. A parametric device model re-creates the headset's sampling, a person-consistent calibration error and spectrally shaped noise, and a frequency-locked diffusion renderer re-textures that high-frequency noise. Converted GazeBase recordings match GazeBaseVR in noise level and accuracy, keep their source's saccade dynamics, and are harder to tell from real VR data than two prior conversions. An authenticator trained only on converted data verifies unseen VR users at a 5.9% equal error rate with 45 s of gaze, close to real VR training (5.8%); with 5 s, a gap remains.