We present a latent talking style space with semantic meanings for speech-driven 3D facial animation. The style space is learned from 3D speech facial animations via a self-supervision paradigm without any style labeling, leading to an automatic separation of high-level attributes, i.e., different channels of the latent style code possess different semantic meanings, such as a wide/slightly open mouth, a grinning/round mouth, and frowning/raising eyebrows. The style space enables intuitive and flexible control of talking styles in speech-driven facial animation through manipulating the channels of style code. To effectively learn such a style space, we propose a two-stage approach, involving two deep neural networks, to disentangle the person identity, speech content, and talking style contained in 3D speech facial animations. The training is performed on a novel dataset of 3D talking faces of various styles, constructed from over ten hours of videos of 200 subjects collected from the Internet.

We present a learning-based approach for generating 3D facial animations with the motion style of a specific subject from arbitrary audio inputs. The subject style is learned from a video clip (1-2 minutes) either downloaded from the Internet or captured through an ordinary camera. Traditional methods often require many hours of the subject's video to learn a robust audio-driven model and are thus unsuitable for this task. Recent research efforts aim to train a model from video collections of a few subjects but ignore the discrimination between the subject style and underlying speech content within facial motions, leading to inaccurate style or articulation. To solve the problem, we propose a novel framework that disentangles subject-specific style and speech content from facial motions. The disentanglement is enabled by two novel training mechanisms. One is two-pass style swapping between two random subjects, and the other is joint training of the decomposition network and audio-to-motion network with a shared decoder. After training, the disentangled style is combined with arbitrary audio inputs to generate stylized audio-driven 3D facial animations. Compared with start-of-the-art methods, our approach achieves better results qualitatively and quantitatively, especially in difficult cases like bilabial plosive and bilabial nasal phonemes.