  Existing GAN inversion methods are stuck in a paradox that the inverted codes
can either achieve high-fidelity reconstruction, or retain the editing
capability. Having only one of them clearly cannot realize real image editing.
In this paper, we resolve this paradox by introducing consecutive images (\eg,
video frames or the same person with different poses) into the inversion
process. The rationale behind our solution is that the continuity of
consecutive images leads to inherent editable directions. This inborn property
is used for two unique purposes: 1) regularizing the joint inversion process,
such that each of the inverted code is semantically accessible from one of the
other and fastened in a editable domain; 2) enforcing inter-image coherence,
such that the fidelity of each inverted code can be maximized with the
complement of other images. Extensive experiments demonstrate that our
alternative significantly outperforms state-of-the-art methods in terms of
reconstruction fidelity and editability on both the real image dataset and
synthesis dataset. Furthermore, our method provides the first support of
video-based GAN inversion, and an interesting application of unsupervised
semantic transfer from consecutive images. Source code can be found at:
\url{https://github.com/cnnlstm/InvertingGANs_with_ConsecutiveImgs}.
