Stochastic Talking Face Generation Using Latent Distribution Matching
This work is significant for researchers and developers in multimedia generation, as it enables more natural and varied talking face synthesis, addressing the limitation of robotic-like single-output systems.
This paper addresses the problem of generating diverse talking faces from a single audio input, moving beyond single-output systems. The authors developed an unsupervised stochastic audio-to-video generation model that captures multiple modes of the video distribution, demonstrating improved performance over baselines on the LRW and GRID datasets.
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.