  The existing methods for audio-driven talking head video editing have the
limitations of poor visual effects. This paper tries to tackle this problem
through editing talking face images seamless with different emotions based on
two modules: (1) an audio-to-landmark module, consisting of the
CrossReconstructed Emotion Disentanglement and an alignment network module. It
bridges the gap between speech and facial motions by predicting corresponding
emotional landmarks from speech; (2) a landmark-based editing module edits face
videos via StyleGAN. It aims to generate the seamless edited video consisting
of the emotion and content components from the input audio. Extensive
experiments confirm that compared with state-of-the-arts methods, our method
provides high-resolution videos with high visual quality.
