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}

翻译：GAN逆映射是将其强大编辑能力应用于真实图像所不可或缺的技术。然而，现有方法逐帧处理视频通常导致时序上的不一致结果。本文提出统一循环框架——RIGID（Recurrent vIdeo GAN Inversion and eDiting），旨在显式且同步地实现真实视频的时序连贯GAN逆映射与面部编辑。该方法从三个方面建模当前帧与历史帧的时序关系：首先，通过学习时序补偿隐码最大化逆映射保真度与一致性，实现高保真视频重建；其次，发现高频域中存在与隐空间可解耦的不连贯噪声；第三，为消除属性操作后的不连贯性，提出帧间组合约束，确保任意帧必须为其相邻帧的直接合成。该统一框架以端到端方式学习输入帧的内在连贯性，因此属性无关，无需重新训练即可对同一视频进行任意编辑。大量实验表明，RIGID在逆映射与编辑任务中均显著优于现有方法。相关成果可见于https://cnnlstm.github.io/RIGID。