DreamTalk: When Expressive Talking Head Generation Meets Diffusion
  Probabilistic Models
Yifeng Ma Shiwei Zhang Jiayu Wang Xiang Wang Yingya Zhang Zhidong Deng
Diffusion models have shown remarkable success in a variety of downstream
generative tasks, yet remain under-explored in the important and challenging
expressive talking head generation. In this work, we propose a DreamTalk
framework to fulfill this gap, which employs meticulous design to unlock the
potential of diffusion models in generating expressive talking heads.
Specifically, DreamTalk consists of three crucial components: a denoising
network, a style-aware lip expert, and a style predictor. The diffusion-based
denoising network is able to consistently synthesize high-quality audio-driven
face motions across diverse expressions. To enhance the expressiveness and
accuracy of lip motions, we introduce a style-aware lip expert that can guide
lip-sync while being mindful of the speaking styles. To eliminate the need for
expression reference video or text, an extra diffusion-based style predictor is
utilized to predict the target expression directly from the audio. By this
means, DreamTalk can harness powerful diffusion models to generate expressive
faces effectively and reduce the reliance on expensive style references.
Experimental results demonstrate that DreamTalk is capable of generating
photo-realistic talking faces with diverse speaking styles and achieving
accurate lip motions, surpassing existing state-of-the-art counterparts.