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/Qingyang-Xu/InvertingGANs_with_ConsecutiveImgs}.

本文提出了一种利用相邻图像进行逆向生成对抗网络 (GAN) 的方法，从而解决现有 GAN 逆向方法的矛盾问题，该方法在保证高保真度的同时可以保留编辑功能，能显著提高重建保真度和可编辑性，并提供了视频 GAN 逆向和无监督语义转移的有趣应用。

从连续性到可编辑性：使用连续图像反转 GANs