MEAD: A Large-Scale Audio-Visual Dataset for Emotional Talking-Face Generation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12366)


The synthesis of natural emotional reactions is an essential criterion in vivid talking-face video generation. This criterion 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), a talking-face video corpus featuring 60 actors and actresses talking with eight different emotions at three different intensity levels. High-quality audio-visual clips are captured at seven different view angles in a strictly-controlled environment. Together with the dataset, we release an emotional talking-face generation baseline that enables the manipulation of both emotion and its intensity. Our dataset could benefit a number of different research fields including conditional generation, cross-modal understanding and expression recognition. Code, model and data are publicly available on our project page \(^{\ddagger }\) \(^{\ddagger }\)


Video generation Generative adversarial networks Representation disentanglement 



This work is supported by the SenseTime-NTU Collaboration Project, Singapore MOE AcRF Tier 1 (2018-T1-002-056), NTU SUG, and NTU NAP.

Supplementary material

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Supplementary material 1 (pdf 7583 KB)

Supplementary material 2 (mp4 36893 KB)


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.SenseTime ResearchBeijingChina
  2. 2.Robotics Institute, Carnegie Mellon UniversityPittsburghUSA
  3. 3.Center for Research on Intelligent Perception and Computing, CASIABeijingChina
  4. 4.University of Chinese Academy of SciencesBeijingChina
  5. 5.Shenzhen Institutes of Advanced Technology, Chinese Academy of ScienceShenzhenChina
  6. 6.Nanyang Technological UniversitySingaporeSingapore

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