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We present an unsupervised approach that converts the input speech of anyindividual into audiovisual streams of potentially-infinitely many outputspeakers. Our approach builds on simple autoencoders that project out-of-sampledata onto the distribution of the training set. We use Exemplar Autoencoders tolearn the voice, stylistic prosody, and visual appearance of a specific targetexemplar speech. In contrast to existing methods, the proposed approach can beeasily extended to an arbitrarily large number of speakers and styles usingonly 3 minutes of target audio-video data, without requiring {\em any} trainingdata for the input speaker. To do so, we learn audiovisual bottleneckrepresentations that capture the structured linguistic content of speech. Weoutperform prior approaches on both audio and video synthesis, and provideextensive qualitative analysis on our project page --https://www.cs.cmu.edu/~exemplar-ae/.

Unsupervised Audiovisual Synthesis via Exemplar Autoencoders