Speech2Face: Learning the Face Behind a Voice
Abstract
How much can we infer about a person's looks from the way they speak? In this paper, we study the task of reconstructing a facial image of a person from a short audio recording of that person speaking. We design and train a deep neural network to perform this task using millions of natural Internet/YouTube videos of people speaking. During training, our model learns voice-face correlations that allow it to produce images that capture various physical attributes of the speakers such as age, gender and ethnicity. This is done in a self-supervised manner, by utilizing the natural co-occurrence of faces and speech in Internet videos, without the need to model attributes explicitly. We evaluate and numerically quantify how--and in what manner--our Speech2Face reconstructions, obtained directly from audio, resemble the true face images of the speakers.
Cite
@article{arxiv.1905.09773,
title = {Speech2Face: Learning the Face Behind a Voice},
author = {Tae-Hyun Oh and Tali Dekel and Changil Kim and Inbar Mosseri and William T. Freeman and Michael Rubinstein and Wojciech Matusik},
journal= {arXiv preprint arXiv:1905.09773},
year = {2019}
}
Comments
To appear in CVPR2019. Project page: http://speech2face.github.io