Invisible Model Identifies Facial Features
Trained on ordinary images, the DINOv3 model unexpectedly began to see faces as a spectrum: on its own, without hints, it isolates the eye, nose, and mouth regions. Like an invisible spectroscope, it breaks down a portrait into parts, just as a prism splits light into colors.
Across 200 videos, feature labeling accuracy reached 83% (random guessing is 23%).