Enhancing Identity-Deformation Disentanglement in StyleGAN for One-Shot Face Video Re-Enactment

Qing Chang, Yao-Xiang Ding, Kun Zhou

Abstract:

The task of one-shot face video re-enactment aims at generating target video of faces with the same identity of one
source frame and facial deformation of the driving video.
To achieve high quality generation, it is essential to precisely disentangle identity-related and identity-independent
characteristics, meanwhile build expressive features keeping high-frequency facial details, which still remain unaddressed for existing approaches. To deal with these two challenges, we propose a two-stage generation model based on
StyleGAN, whose key novel techniques lie in better disentangling identity and deformation codes in the latent space
through an identity-based modeling and manipulating intermediate StyleGAN features at the second stage for augmenting facial details of the generating targets. To further improve identity consistency, a data augmentation method is introduced during training for enhancing the key features affecting identity such as hair and wrinkles. Extensive experimental results demonstrate the superiority of our approach
compared to state-of-the-art methods. Code is available at
https://anonymous.4open.science/r/ViVFace-CBD3