We present a method for generating a video of a talking face. The method takes as inputs: (i) still images of the target face, and (ii) an audio speech segment; and outputs 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 propose an encoder-decoder CNN model that uses a joint embedding of the face and audio to generate synthesised talking face video frames. The model is trained on tens of hours of unlabelled videos. We also show results of re-dubbing videos using speech from a different person. Figure 1: The Speech2Vid model generates a video of a talking face, given still images of the person and a speech segment. The model takes an image of the target face and an audio segment, and outputs a video of the target face lip synched with the audio. Note that the target face need not be in the training dataset i.e. the Speech2Vid is applicable to unseen images and speech.
Chung et al. REF learned a joint embedding of the face and audio signal, and use an encoder-decoder CNN model to generate talking face video.
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ArXiv
Computer Science