MODA: Mapping-Once Audio-driven Portrait Animation with Dual Attentions
Yunfei Liu Lijian Lin Fei Yu Changyin Zhou Yu Li
Audio-driven portrait animation aims to synthesize portrait videos that are
conditioned by given audio. Animating high-fidelity and multimodal video
portraits has a variety of applications. Previous methods have attempted to
capture different motion modes and generate high-fidelity portrait videos by
training different models or sampling signals from given videos. However,
lacking correlation learning between lip-sync and other movements (e.g., head
pose/eye blinking) usually leads to unnatural results. In this paper, we
propose a unified system for multi-person, diverse, and high-fidelity talking
portrait generation. Our method contains three stages, i.e., 1) Mapping-Once
network with Dual Attentions (MODA) generates talking representation from given
audio. In MODA, we design a dual-attention module to encode accurate mouth
movements and diverse modalities. 2) Facial composer network generates dense
and detailed face landmarks, and 3) temporal-guided renderer syntheses stable
videos. Extensive evaluations demonstrate that the proposed system produces
more natural and realistic video portraits compared to previous methods.