Lightweight Eye Capture Using a Parametric Model

We present the first approach for high-quality lightweight eye capture, which leverages a database of pre-captured eyes to guide the reconstruction of new eyes from much less constrained inputs, such as traditional single-shot face scanners or even a single photo from the internet.

Facial scanning has become ubiquitous in digital media, but so far most efforts have focused on reconstructing the skin. Eye reconstruction, on the other hand, has received only little attention, and the current state-of-the-art method is cumbersome for the actor, time-consuming, and requires carefully setup and calibrated hardware. These constraints currently make eye capture impractical for general use. We present the first approach for high-quality lightweight eye capture, which leverages a database of pre-captured eyes to guide the reconstruction of new eyes from much less constrained inputs, such as traditional single-shot face scanners or even a single photo from the internet. This is accomplished with a new parametric model of the eye built from the database, and a novel image-based model fitting algorithm. Our method provides both automatic reconstructions of real eyes, as well as artistic control over the parameters to generate user-specific eyes.