The generation of emotional talking faces from a single portrait image
remains a significant challenge. The simultaneous achievement of expressive
emotional talking and accurate lip-sync is particularly difficult, as
expressiveness is often compromised for the accuracy of lip-sync. As widely
adopted by many prior works, the LSTM network often fails to capture the
subtleties and variations of emotional expressions. To address these
challenges, we introduce DREAM-Talk, a two-stage diffusion-based audio-driven
framework, tailored for generating diverse expressions and accurate lip-sync
concurrently. In the first stage, we propose EmoDiff, a novel diffusion module
that generates diverse highly dynamic emotional expressions and head poses in
accordance with the audio and the referenced emotion style. Given the strong
correlation between lip motion and audio, we then refine the dynamics with
enhanced lip-sync accuracy using audio features and emotion style. To this end,
we deploy a video-to-video rendering module to transfer the expressions and lip
motions from our proxy 3D avatar to an arbitrary portrait. Both quantitatively
and qualitatively, DREAM-Talk outperforms state-of-the-art methods in terms of
expressiveness, lip-sync accuracy and perceptual quality.

DREAM-Talk 是一个两阶段扩散式音频驱动框架，通过 EmoDiff 生成多样的动态情感表达和姿态，并使用音频特征和情感风格来提升唇部同步准确性，通过视频到视频渲染模块将情感和唇部运动从虚拟 3D 头像传输到任意肖像，在表达性、唇部同步准确性和感知质量方面超过现有方法。