Generating realistic talking faces is a complex and widely discussed task
with numerous applications. In this paper, we present DiffTalker, a novel model
designed to generate lifelike talking faces through audio and landmark
co-driving. DiffTalker addresses the challenges associated with directly
applying diffusion models to audio control, which are traditionally trained on
text-image pairs. DiffTalker consists of two agent networks: a
transformer-based landmarks completion network for geometric accuracy and a
diffusion-based face generation network for texture details. Landmarks play a
pivotal role in establishing a seamless connection between the audio and image
domains, facilitating the incorporation of knowledge from pre-trained diffusion
models. This innovative approach efficiently produces articulate-speaking
faces. Experimental results showcase DiffTalker's superior performance in
producing clear and geometrically accurate talking faces, all without the need
for additional alignment between audio and image features.

通过音频和地标联动，DiffTalker 模型可以生成逼真的说话人脸。DiffTalker 通过两种代理网络实现对几何精确性和纹理细节的处理，从而有效地生成清晰而几何精确的说话人脸。