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Speech-driven 3D facial animation has gained significant attention for itsability to create realistic and expressive facial animations in 3D space basedon speech. Learning-based methods have shown promising progress in achievingaccurate facial motion synchronized with speech. However, one-to-many nature ofspeech-to-3D facial synthesis has not been fully explored: while the lipaccurately synchronizes with the speech content, other facial attributes beyondspeech-related motions are variable with respect to the speech. To account forthe potential variance in the facial attributes within a single speech, wepropose DF-3DFace, a diffusion-driven speech-to-3D face mesh synthesis.DF-3DFace captures the complex one-to-many relationships between speech and 3Dface based on diffusion. It concurrently achieves aligned lip motion byexploiting audio-mesh synchronization and masked conditioning. Furthermore, theproposed method jointly models identity and pose in addition to facial motionsso that it can generate 3D face animation without requiring a referenceidentity mesh and produce natural head poses. We contribute a new large-scale3D facial mesh dataset, 3D-HDTF to enable the synthesis of variations inidentities, poses, and facial motions of 3D face mesh. Extensive experimentsdemonstrate that our method successfully generates highly variable facialshapes and motions from speech and simultaneously achieves more realisticfacial animation than the state-of-the-art methods.

DF-3DFace: One-to-Many Speech Synchronized 3D Face Animation with Diffusion