  There have been a number of techniques that have demonstrated the generation
of multimedia data for one modality at a time using GANs, such as the ability
to generate images, videos, and audio. However, so far, the task of multi-modal
generation of data, specifically for audio and videos both, has not been
sufficiently well-explored. Towards this, we propose a method that demonstrates
that we are able to generate naturalistic samples of video and audio data by
the joint correlated generation of audio and video modalities. The proposed
method uses multiple discriminators to ensure that the audio, video, and the
joint output are also indistinguishable from real-world samples. We present a
dataset for this task and show that we are able to generate realistic samples.
This method is validated using various standard metrics such as Inception
Score, Frechet Inception Distance (FID) and through human evaluation.
