Abstract We present a novel parametric eye rig for eye animation, including a new multi‐view imaging system that can reconstruct eye poses at submillimeter accuracy to which we fit our new rig. This allows us to accurately estimate person‐specific eyeball shape, rotation center, interocular distance, visual axis, and other rig parameters resulting in an animation‐ready eye rig. We demonstrate the importance of several aspects of eye modeling that are often overlooked, for example that the visual axis is not identical to the optical axis, that it is important to model rotation about the optical axis, and that the rotation center of the eye should be measured accurately for each person. Since accurate rig fitting requires hand annotation of multi‐view imagery for several eye gazes, we additionally propose a more user‐friendly “lightweight” fitting approach, which leverages an average rig created from several pre‐captured accurate rigs. Our lightweight rig fitting method allows for the estimation of eyeball shape and eyeball position given only a single pose with a known look‐at point (e.g. looking into a camera) and few manual annotations.

In computer graphics the motion of the jaw is commonly modelled by up-down and left-right rotation around a fixed pivot plus a forward-backward translation, yielding a three dimensional rig that is highly suited for intuitive artistic control. The anatomical motion of the jaw is, however, much more complex since the joints that connect the jaw to the skull exhibit both rotational and translational components. In reality the jaw does not move in a three dimensional subspace but on a constrained manifold in six dimensions. We analyze this manifold in the context of computer animation and show how the manifold can be parameterized with three degrees of freedom, providing a novel jaw rig that preserves the intuitive control while providing more accurate jaw positioning. The chosen parameterization furthermore places anatomically correct limits on the motion, preventing the rig from entering physiologically infeasible poses. Our new jaw rig is empirically designed from accurate capture data, and we provide a simple method to retarget the rig to new characters, both human and fantasy.

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.

Even though the human eye is one of the central features of individual appearance, its shape has so far been mostly approximated in our community with gross simplifications. In this paper we demonstrate that there is a lot of individuality to every eye, a fact that common practices for 3D eye generation do not consider. To faithfully reproduce all the intricacies of the human eye we propose a novel capture system that is capable of accurately reconstructing all the visible parts of the eye: the white sclera , the transparent cornea and the non-rigidly deforming colored iris . These components exhibit very different appearance properties and thus we propose a hybrid reconstruction method that addresses them individually, resulting in a complete model of both spatio-temporal shape and texture at an unprecedented level of detail, enabling the creation of more believable digital humans. Finally, we believe that the findings of this paper will alter our community's current assumptions regarding human eyes, and our work has the potential to significantly impact the way that eyes will be modelled in the future.