Beyond the Black Patch: A Composable, Gaze-Contingent Framework for Simulating Central Vision Loss in XR.

Authors

Parisa Ghasemi, Susan A Primo, Carolyn C Seepersad, Mohsen Moghaddam

Published in

IEEE transactions on visualization and computer graphics. Volume PP. Nov 12, 2026. Epub Nov 12, 2026.

Abstract

Central vision loss (CVL) from macular diseases such as age-related macular degeneration and Stargardt disease is commonly simulated in extended reality (XR) using a single mechanism, typically a dark patch or blur overlay. Yet clinical reports suggest that people with CVL reject this depiction and instead report a broad range of perceptual effects, including spatial distortion, perceptual invisibility, dynamic noise, dimming, filling-in, and temporal disturbances. We present XR-VIEU (e $\underline{X}$ tended $\underline{R}$eality for $\underline{V}$isual $\underline{I}$mpairment $\underline{E}$xploration & $\underline{U}$nderstanding), a modular, real-time, gaze-contingent framework for simulating CVL in XR through seven clinically grounded, independent, composable shader modules implemented in Unity HDRP, with formal mathematical specifications and over 120 configurable parameters per eye. To probe whether our simulation can capture lived CVL experiences, we conducted a profile reconstruction design study with two individuals with late-stage Stargardt disease. Despite sharing the same diagnosis, the two participants required significantly different module configurations: one profile was dominated by content displacement and dimming, while the other relied primarily on dense fractal noise. These cases provide initial evidence that modular, composable simulation is needed to represent the heterogeneity of CVL experience. The full seven-module pipeline adds only 0.20 ms of rendering overhead (2.9%) while maintaining approximately 90 Hz on the Varjo XR-4 at native resolution. XR-VIEU provides a foundation for more faithful and personalized CVL simulation in research, clinical education, and accessibility-oriented design.

PMID:
43019259
Bibliographic data and abstract were imported from PubMed on 13 Nov 2026.

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