GAN inversion is indispensable for applying the powerful editability of GAN to real images. However, existing methods invert video frames individually often leading to undesired inconsistent results over time. In this paper, we propose a unified recurrent framework, named \textbf{R}ecurrent v\textbf{I}deo \textbf{G}AN \textbf{I}nversion and e\textbf{D}iting (RIGID), to explicitly and simultaneously enforce temporally coherent GAN inversion and facial editing of real videos. Our approach models the temporal relations between current and previous frames from three aspects. To enable a faithful real video reconstruction, we first maximize the inversion fidelity and consistency by learning a temporal compensated latent code. Second, we observe incoherent noises lie in the high-frequency domain that can be disentangled from the latent space. Third, to remove the inconsistency after attribute manipulation, we propose an \textit{in-between frame composition constraint} such that the arbitrary frame must be a direct composite of its neighboring frames. Our unified framework learns the inherent coherence between input frames in an end-to-end manner, and therefore it is agnostic to a specific attribute and can be applied to arbitrary editing of the same video without re-training. Extensive experiments demonstrate that RIGID outperforms state-of-the-art methods qualitatively and quantitatively in both inversion and editing tasks. The deliverables can be found in \url{https://cnnlstm.github.io/RIGID}

本文提出了一种名为RIGID（Recurrent vIGAN Inversion and eDiting）的统一循环框架，用于显式并同时对实际视频进行时间一致的GAN反演和面部编辑。通过从三个方面建模当前帧与前一帧之间的时间关系，我们的方法可以实现忠实的实际视频重构。该统一框架以端到端的方式学习输入帧之间的内在一致性，因此对于特定属性是不可知的，并且可以用于同一视频的任意编辑而无需重新训练。经过广泛实验证明，RIGID在反演和编辑任务中在定性和定量方面均优于现有的最先进方法。

RIGID: 实时人脸视频的循环生成对抗网络还原和编辑