  People talk with diversified styles. For one piece of speech, different
talking styles exhibit significant differences in the facial and head pose
movements. For example, the "excited" style usually talks with the mouth wide
open, while the "solemn" style is more standardized and seldomly exhibits
exaggerated motions. Due to such huge differences between different styles, it
is necessary to incorporate the talking style into audio-driven talking face
synthesis framework. In this paper, we propose to inject style into the talking
face synthesis framework through imitating arbitrary talking style of the
particular reference video. Specifically, we systematically investigate talking
styles with our collected \textit{Ted-HD} dataset and construct style codes as
several statistics of 3D morphable model~(3DMM) parameters. Afterwards, we
devise a latent-style-fusion~(LSF) model to synthesize stylized talking faces
by imitating talking styles from the style codes. We emphasize the following
novel characteristics of our framework: (1) It doesn't require any annotation
of the style, the talking style is learned in an unsupervised manner from
talking videos in the wild. (2) It can imitate arbitrary styles from arbitrary
videos, and the style codes can also be interpolated to generate new styles.
Extensive experiments demonstrate that the proposed framework has the ability
to synthesize more natural and expressive talking styles compared with baseline
methods.
