A review on data-driven learning of a talking head model

Abstract

Constructing a talking head model of a person allows generation of a novel talking face animation from an unseen audio sequence of the person. This has important applications such as building virtual avatars of people that can interact with real people in novel situations, model-based video compression, teleconferencing, human-computer interaction, computer graphics and video games. Traditionally, talking head models have been built by manual painstaking work. The advancement of computer vision and machine learning techniques, especially in the past decade, has made possible the automatic learning of a talking head model of a person from data. In this paper, we focus on this area of machine learning based data-driven facial animation and critically review the most common approaches, compare and contrast among them and identify promising research directions and prospects.