Controllable face editing for video reconstruction in human digital twins

作者：

Highlights：

• Controllable face editing to videos with high resolution at 1920*1080.

• Semantic inversion networks trained for disentangled facial editing.

• Personalized latent spaces trained for identity preservation.

• Outperforming current state-of-the-art works in extensive experiments.

摘要

•Controllable face editing to videos with high resolution at 1920*1080.•Semantic inversion networks trained for disentangled facial editing.•Personalized latent spaces trained for identity preservation.•Outperforming current state-of-the-art works in extensive experiments.

论文关键词：Human digital twins,Video reconstruction,Controllable face editing,Generative adversarial networks

论文评审过程：Received 19 December 2021, Revised 6 May 2022, Accepted 30 June 2022, Available online 5 July 2022, Version of Record 11 July 2022.

论文官网地址：https://doi.org/10.1016/j.imavis.2022.104517