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.
@article{arxiv.2102.03984,
title = {One-shot Face Reenactment Using Appearance Adaptive Normalization},
author = {Guangming Yao and Yi Yuan and Tianjia Shao and Shuang Li and Shanqi Liu and Yong Liu and Mengmeng Wang and Kun Zhou},
journal= {arXiv preprint arXiv:2102.03984},
year = {2021}
}