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Visual emotion expression plays an important role in audiovisual speechcommunication. In this work, we propose a novel approach to rendering visualemotion expression in speech-driven talking face generation. Specifically, wedesign an end-to-end talking face generation system that takes a speechutterance, a single face image, and a categorical emotion label as input torender a talking face video synchronized with the speech and expressing theconditioned emotion. Objective evaluation on image quality, audiovisualsynchronization, and visual emotion expression shows that the proposed systemoutperforms a state-of-the-art baseline system. Subjective evaluation of visualemotion expression and video realness also demonstrates the superiority of theproposed system. Furthermore, we conduct a human emotion recognition pilotstudy using generated videos with mismatched emotions among the audio andvisual modalities. Results show that humans respond to the visual modality moresignificantly than the audio modality on this task.

Speech Driven Talking Face Generation from a Single Image and an Emotion Condition