  Although existing speech-driven talking face generation methods achieve
significant progress, they are far from real-world application due to the
avatar-specific training demand and unstable lip movements. To address the
above issues, we propose the GSmoothFace, a novel two-stage generalized talking
face generation model guided by a fine-grained 3d face model, which can
synthesize smooth lip dynamics while preserving the speaker's identity. Our
proposed GSmoothFace model mainly consists of the Audio to Expression
Prediction (A2EP) module and the Target Adaptive Face Translation (TAFT)
module. Specifically, we first develop the A2EP module to predict expression
parameters synchronized with the driven speech. It uses a transformer to
capture the long-term audio context and learns the parameters from the
fine-grained 3D facial vertices, resulting in accurate and smooth
lip-synchronization performance. Afterward, the well-designed TAFT module,
empowered by Morphology Augmented Face Blending (MAFB), takes the predicted
expression parameters and target video as inputs to modify the facial region of
the target video without distorting the background content. The TAFT
effectively exploits the identity appearance and background context in the
target video, which makes it possible to generalize to different speakers
without retraining. Both quantitative and qualitative experiments confirm the
superiority of our method in terms of realism, lip synchronization, and visual
quality. See the project page for code, data, and request pre-trained models:
https://zhanghm1995.github.io/GSmoothFace.
