We present GStalker, a 3D audio-driven talking face generation model with
Gaussian Splatting for both fast training (40 minutes) and real-time rendering
(125 FPS) with a 3$\sim$5 minute video for training material, in comparison
with previous 2D and 3D NeRF-based modeling frameworks which require hours of
training and seconds of rendering per frame. Specifically, GSTalker learns an
audio-driven Gaussian deformation field to translate and transform 3D Gaussians
to synchronize with audio information, in which multi-resolution hashing
grid-based tri-plane and temporal smooth module are incorporated to learn
accurate deformation for fine-grained facial details. In addition, a
pose-conditioned deformation field is designed to model the stabilized torso.
To enable efficient optimization of the condition Gaussian deformation field,
we initialize 3D Gaussians by learning a coarse static Gaussian representation.
Extensive experiments in person-specific videos with audio tracks validate that
GSTalker can generate high-fidelity and audio-lips synchronized results with
fast training and real-time rendering speed.

通过 Gaussian Splatting 进行 3D 音频驱动的说话人脸生成模型的快速训练和实时渲染，可在短时间内生成高保真度和音频同步的结果。