One-shot Face Reenactment Using Appearance Adaptive Normalization

Authors

  • Guangming Yao NetEase Fuxi AI Lab
  • Yi Yuan NetEase Fuxi AI Lab
  • Tianjia Shao State Key Lab of CAD&CG, Zhejiang University
  • Shuang Li School of Computer Science and Technology, Beijing Institute of Technology
  • Shanqi Liu Institute of Cyber-Systems and Control, Zhejiang University
  • Yong Liu Institute of Cyber-Systems and Control, Zhejiang University
  • Mengmeng Wang Institute of Cyber-Systems and Control, Zhejiang University
  • Kun Zhou State Key Lab of CAD&CG, Zhejiang University

Keywords:

Computational Photography, Image & Video Synthesis, Applications

Abstract

The paper proposes a novel generative adversarial network for one-shot face reenactment, which can animate a single face image to a different pose-and-expression (provided by a driving image) while keeping its original appearance. The core of our network is a novel mechanism called appearance adaptive normalization, which can effectively integrate the appearance information from the input image into our face generator by modulating the feature maps of the generator using the learned adaptive parameters. Furthermore, we specially design a local net to reenact the local facial components (i.e., eyes, nose and mouth) first, which is a much easier task for the network to learn and can in turn provide explicit anchors to guide our face generator to learn the global appearance and pose-and-expression. Extensive quantitative and qualitative experiments demonstrate the significant efficacy of our model compared with prior one-shot methods.

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Published

2021-05-18

How to Cite

Yao, G., Yuan, Y., Shao, T., Li, S., Liu, S., Liu, Y., Wang, M., & Zhou, K. (2021). One-shot Face Reenactment Using Appearance Adaptive Normalization. Proceedings of the AAAI Conference on Artificial Intelligence, 35(4), 3172-3180. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16427

Issue

Section

AAAI Technical Track on Computer Vision III