Abstract
Visual emotion expression plays an important role in audiovisual speech communication. In this work, we propose a novel approach to rendering visual emotion expression in speech-driven talking face generation. Specifically, we design an end-to-end talking face generation system that takes a speech utterance, a single face image, and a categorical emotion label as input to render a talking face video in sync with the speech and expressing the condition emotion. Objective evaluation on image quality, audiovisual synchronization, and visual emotion expression shows that the proposed system outperforms a state-of-the-art baseline system. Subjective evaluation of visual emotion expression and video realness also demonstrates the superiority of the proposed system. Furthermore, we conduct a pilot study on human emotion recognition of generated videos with mismatched emotions between the audio and visual modalities, and results show that humans reply on the visual modality more significantly than the audio modality on this task.
Abstract (translated)
URL
https://arxiv.org/abs/2008.03592
