Facial expression GAN for voice-driven face generation

Abstract

Cross-modal audiovisual generation is an emerging topic in machine learning. In particular, voice-to-face is one of the most popular research branches, which aims to generate faces from human voice clips. Most recent works in voice-to-face generation do not take emotion information into account. However, it could be widely observed that expressions are the key face attributes to reconstruct sharper and more discriminative faces. In this paper, we propose a novel facial expression GAN (FE-GAN) which takes emotion and expressions into account in face generation. To achieve this goal, we use two auxiliary classifiers to learn more emotion and identity representations between different modalities, respectively. Moreover, we design two discriminators, each focusing on a different aspect of the faces, to measure identity and emotion semantic relevance in generating. The triple loss is designed to make FE-GAN robust to voice variety and keep balance in two different modalities. Extensive experiments are conducted on two real datasets to demonstrate the effectiveness of FE-GAN in both quantitative and qualitative perspectives. The experimental results show that FE-GAN can not only outperform the previous models in terms of FID and IS values, but also generate more realistic face images compared with previous models.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant 61761166005), Ministry of Science and Technology, Taiwan (MOST 106-2218-E-032-003-MY3), and National Natural Science Foundation of Zhejiang (Grant LY20F020007), and the Ningbo Science Technology Plan projects (Grant 2019B10032) and the K.C. Wong Magna Fund in Ningbo University.

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Correspondence to Zhen Liu.

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Fang, Z., Liu, Z., Liu, T. et al. Facial expression GAN for voice-driven face generation. Vis Comput (2021). https://doi.org/10.1007/s00371-021-02074-w

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Keywords

  • Expression reconstruction
  • Cross-model generation
  • Voice-to-face generation
  • Generative adversarial networks