  Despite previous success in generating audio-driven talking heads, most of
the previous studies focus on the correlation between speech content and the
mouth shape. Facial emotion, which is one of the most important features on
natural human faces, is always neglected in their methods. In this work, we
present Emotional Video Portraits (EVP), a system for synthesizing high-quality
video portraits with vivid emotional dynamics driven by audios. Specifically,
we propose the Cross-Reconstructed Emotion Disentanglement technique to
decompose speech into two decoupled spaces, i.e., a duration-independent
emotion space and a duration dependent content space. With the disentangled
features, dynamic 2D emotional facial landmarks can be deduced. Then we propose
the Target-Adaptive Face Synthesis technique to generate the final high-quality
video portraits, by bridging the gap between the deduced landmarks and the
natural head poses of target videos. Extensive experiments demonstrate the
effectiveness of our method both qualitatively and quantitatively.
