You said that?: Synthesising talking faces from audio

Journal article

We describe a method for generating a video of a talking face. The method takes still images of the target face and an audio speech segment as inputs, and generates a video of the target face lip synched with the audio. The method runs in real time and is applicable to faces and audio not seen at training time. To achieve this we develop an encoder–decoder convolutional neural network (CNN) model that uses a joint embedding of the face and audio to generate synthesised talking face video frames. The model is trained on unlabelled videos using cross-modal self-supervision. We also propose methods to re-dub videos by visually blending the generated face into the source video frame using a multi-stream CNN model.

Authors: Jamaludin, A; Chung, JS; Zisserman, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: International Journal of Computer Vision
• Volume: 127
• Issue: Special Issue on Machine Vision
• Pages: 1767–1779
• Publication date: 2019-02-13
• Acceptance date: 2019-01-16
• DOI: 10.1007/s11263-019-01150-y
• EISSN: 1573-1405
• ISSN: 0920-5691
• Language: English
• Keywords: FFR; machine learning; video synthesis; visual speech synthesis; computer vision