Stochastic Talking Face Generation Using Latent Distribution Matching

Autor:	Yadav, Ravindra, Sardana, Ashish, Namboodiri, Vinay P, Hegde, Rajesh M
Rok vydání:	2020
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: InterSpeech 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2011.10727 Zobrazit plný text záznamu View this record from Arxiv