| dc.description.abstract | We present a real-time gaze animation system using recurrent neural networks. Both motion capture data and video from a head-mounted
camera are used to train the network to predict the motion of the body and the eyes. The system is trained separately
on different poses, e.g. standing, sitting, and laying down, and is able to learn constraints on movement per pose. A simplified
version of the neural network is presented, for scenarios which allow for lower detail gaze animation. We compare various
neural network architectures and show that our method has the capability to learn realistic gaze motion from the data, while
maintaining performance. Results from a user study conducted among game industry professionals, shows that our method
significantly improves perceived naturalness of the gaze animation, compared to a manually created procedural gaze system. | |
