Towards Expressively Speech-Driven Facial Animation

In order to synthesize facial expression details such as eye blinking and eyebrow-lifting,this paper presents a new speech-driven facial animation method.This method contains two phases,training and synthesizing phases.During the training phase,3D facial animation data is firstly re-sampled to improve training efficiency.Then a hidden Markov model(HMM) is utilized to study the correlation between the expressive facial animation features and the synchronized speeches.At the same time,statistical data of the synthetic residuals is collected from the trained HMM.During the synthesizing phase,firstly,the trained HMM is used to estimate the matching expressive facial animation from the novel input speech features.Secondly,based on the estimated animation,expression details are synthesized using the collected residuals.Numerical and user study experiments show that this method outperforms conventional approaches both in the efficiency and animation quality.