  Achieving high synchronization in the synthesis of realistic, speech-driven
talking head videos presents a significant challenge. Traditional Generative
Adversarial Networks (GAN) struggle to maintain consistent facial identity,
while Neural Radiance Fields (NeRF) methods, although they can address this
issue, often produce mismatched lip movements, inadequate facial expressions,
and unstable head poses. A lifelike talking head requires synchronized
coordination of subject identity, lip movements, facial expressions, and head
poses. The absence of these synchronizations is a fundamental flaw, leading to
unrealistic and artificial outcomes. To address the critical issue of
synchronization, identified as the "devil" in creating realistic talking heads,
we introduce SyncTalk. This NeRF-based method effectively maintains subject
identity, enhancing synchronization and realism in talking head synthesis.
SyncTalk employs a Face-Sync Controller to align lip movements with speech and
innovatively uses a 3D facial blendshape model to capture accurate facial
expressions. Our Head-Sync Stabilizer optimizes head poses, achieving more
natural head movements. The Portrait-Sync Generator restores hair details and
blends the generated head with the torso for a seamless visual experience.
Extensive experiments and user studies demonstrate that SyncTalk outperforms
state-of-the-art methods in synchronization and realism. We recommend watching
the supplementary video: https://ziqiaopeng.github.io/synctalk
