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This work addresses the need for efficient and adaptable video synthesis for applications like re-dubbing, though it appears incremental in its approach.
The paper tackles the problem of generating realistic talking face videos from a still image and audio input, achieving real-time performance and generalization to unseen faces and audio.
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.