MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation
Kaisiyuan Wang Qianyi Wu Linsen Song Zhuoqian Yang Wayne Wu Chen Qian Ran He Yu Qiao Chen Change Loy
Abstract
The synthesis of natural emotional reactions is an essentialcriteria in vivid talking-face video generation. This criteria is nevertheless seldom taken into consideration in previous works due to the absence of a large-scale, high-quality emotional audio-visual dataset. To address this issue, we build the Multi-view Emotional Audio-visual Dataset(MEAD) which is a talking-face video corpus featuring 60 actors and actresses talking with 8 different emotions at 3 different intensity levels. High-quality audio-visual clips are captured at 7 different view angles in a strictly-controlled environment. Together with the dataset, we release an emotional talking-face generation baseline which enables the manipulation of both emotion and its intensity. Our dataset will be made public and could benefit a number of different research fields including conditional generation, cross-modal understanding and expression recognition."
BibTeX
@inproceedings{eccv2020_meadalargescalea,
title = {MEAD: A Large-scale Audio-visual Dataset for Emotional Talking-face Generation},
author = {Kaisiyuan Wang Qianyi Wu Linsen Song Zhuoqian Yang Wayne Wu Chen Qian Ran He Yu Qiao Chen Change Loy},
booktitle = {ECCV 2020},
year = {2020}
}