Flexible expressions could lift 3D-generated faces out of the uncanny valley

by admin November 26, 2020 at 6:36 am

3D-rendered faces are a big part of any major movie or game now, but the task of capturing and animating them in a natural way can be a tough one. Disney Research is working on ways to smooth out this process, among them a machine learning tool that makes it much easier to generate and manipulate 3D faces without dipping into the uncanny valley.

Of course this technology has come a long way from the wooden expressions and limited details of earlier days. High-resolution, convincing 3D faces can be animated quickly and well, but the subtleties of human expression are not just limitless in variety, they’re very easy to get wrong.

Think of how someone’s entire face changes when they smile — it’s different for everyone, but there are enough similarities that we fancy we can tell when someone is “really” smiling or just faking it. How can you achieve that level of detail in an artificial face?

Existing “linear” models simplify the subtlety of expression, making “happiness” or “anger” minutely adjustable, but at the cost of accuracy — they can’t express every possible face, but can easily result in impossible faces. Newer neural models learn complexity from watching the interconnectedness of expressions, but like other such models their workings are obscure and difficult to control, and perhaps not generalizable beyond the faces they learned from. They don’t enable the level of control an artist working on a movie or game needs, or result in faces that (humans are remarkably good at detecting this) are just off somehow.