LatentSync

LatentSync applies latent diffusion techniques specifically to the lip sync task, generating lip-synced video by iteratively denoising in a compressed latent space. The model conditions the diffusion process on both audio features and the source face, producing output that maintains the speaker's identity while accurately synchronizing mouth movements to speech.

By operating in latent space, LatentSync achieves a favorable balance between visual quality and computational efficiency. The model represents the convergence of diffusion-based generation quality with practical inference speeds, moving diffusion-based lip sync closer to production viability.

LatentSync relates to several other concepts in the AI lip sync pipeline: Latent Diffusion , and Diffusion Models .