  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}
