3DiFACE: Diffusion-based Speech-driven 3D Facial Animation and Editing
Balamurugan Thambiraja Sadegh Aliakbarian Darren Cosker Justus Thies
We present 3DiFACE, a novel method for personalized speech-driven 3D facial
animation and editing. While existing methods deterministically predict facial
animations from speech, they overlook the inherent one-to-many relationship
between speech and facial expressions, i.e., there are multiple reasonable
facial expression animations matching an audio input. It is especially
important in content creation to be able to modify generated motion or to
specify keyframes. To enable stochasticity as well as motion editing, we
propose a lightweight audio-conditioned diffusion model for 3D facial motion.
This diffusion model can be trained on a small 3D motion dataset, maintaining
expressive lip motion output. In addition, it can be finetuned for specific
subjects, requiring only a short video of the person. Through quantitative and
qualitative evaluations, we show that our method outperforms existing
state-of-the-art techniques and yields speech-driven animations with greater
fidelity and diversity.