Stochastic Talking Face Generation Using Latent Distribution Matching

TL;DR

This paper introduces an unsupervised stochastic model for diverse talking face generation from a single audio input, capturing multiple plausible lip-synced videos using a multi-modal variational autoencoder framework.

Contribution

It presents a novel unsupervised stochastic audio-to-video generation approach that models multiple modes of video distribution for diverse talking face synthesis.

Findings

Outperforms baseline methods on LRW and GRID datasets.

Generates multiple plausible lip-synced videos from a single audio.

Effectively captures diverse facial movements in generated videos.

Abstract

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.