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We present 3DiFACE, a novel method for personalized speech-driven 3D facialanimation and editing. While existing methods deterministically predict facialanimations from speech, they overlook the inherent one-to-many relationshipbetween speech and facial expressions, i.e., there are multiple reasonablefacial expression animations matching an audio input. It is especiallyimportant in content creation to be able to modify generated motion or tospecify keyframes. To enable stochasticity as well as motion editing, wepropose a lightweight audio-conditioned diffusion model for 3D facial motion.This diffusion model can be trained on a small 3D motion dataset, maintainingexpressive lip motion output. In addition, it can be finetuned for specificsubjects, requiring only a short video of the person. Through quantitative andqualitative evaluations, we show that our method outperforms existingstate-of-the-art techniques and yields speech-driven animations with greaterfidelity and diversity.

3DiFACE: Diffusion-based Speech-driven 3D Facial Animation and Editing