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

SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis