  The ability to envisage the visual of a talking face based just on hearing a
voice is a unique human capability. There have been a number of works that have
solved for this ability recently. We differ from these approaches by enabling a
variety of talking face generations based on single audio input. Indeed, just
having the ability to generate a single talking face would make a system almost
robotic in nature. In contrast, our unsupervised stochastic audio-to-video
generation model allows for diverse generations from a single audio input.
Particularly, we present an unsupervised stochastic audio-to-video generation
model that can capture multiple modes of the video distribution. We ensure that
all the diverse generations are plausible. We do so through a principled
multi-modal variational autoencoder framework. We demonstrate its efficacy on
the challenging LRW and GRID datasets and demonstrate performance better than
the baseline, while having the ability to generate multiple diverse lip
synchronized videos.
