Calibration-Free Gaze Tracking
Gaze tracking has been "almost ready" for consumer deployment for a decade. The Achilles heel: calibration. Before you get accurate estimates, users must stare at 9, 13, or 25 fixation points while a model learns their personal geometry.
Beyond the UX friction, this creates a privacy requirement — you must store per-user calibration profiles. In healthcare and defense applications, this is often untenable.
The Synthetic Data Approach
We bypassed the calibration problem by removing the need for personalization altogether.
Using a differentiable eye renderer parameterized over 180 anatomical variables (iris size, limbal ring, sclera reflectance, eyelid fold geometry, corneal curvature), we generated 4.2M unique synthetic eyes across 400,000 simulated lighting conditions. We trained a compact ViT-Tiny encoder to regress gaze direction from raw infrared frames — 30×30 pixels, 60fps, 12mW on an ARM Cortex-A55.
Accuracy
On our held-out set of 312 real users (not seen during training):
Why This Matters
Gaze is the highest-bandwidth implicit signal a human emits — 4 saccades per second, each carrying intent. When you can read it without friction, without personalization, without a privacy footprint, the interface design space opens up completely.