Data-driven gaze animation using recurrent neural networks

Abstract

We present a data-driven gaze animation method using recurrent neural networks. The neural network is trained with motion capture data including different poses such as standing, sitting, and lying down and is able to learn the constraints related with each particular pose. A simplified version of the neural network is also presented for Level of Detail (LOD) animation. We compare various neural network architectures and show that our method produces natural gaze motion in real-time. Results from a user study conducted among game industry professionals shows that our method has better perceived naturalness compared to the procedural gaze animation system of a well-known game company. Our approach is the first one to show the feasibility of gaze motions using deep neural networks.