Speech2Face: Learning the Face Behind a Voice

Tae-Hyun Oh, Tali Dekel, Changil Kim, Inbar Mosseri, William T. Freeman, Michael Rubinstein, Wojciech Matusik; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 7539-7548

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.

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[bibtex]
@InProceedings{Oh_2019_CVPR,
author = {Oh, Tae-Hyun and Dekel, Tali and Kim, Changil and Mosseri, Inbar and Freeman, William T. and Rubinstein, Michael and Matusik, Wojciech},
title = {Speech2Face: Learning the Face Behind a Voice},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}