RIGID：真实人脸视频的循环 GAN 反演与编辑
RIGID: Recurrent GAN Inversion and Editing of Real Face Videos
AI总结：
本文提出 RIGID 循环框架，通过时间补偿潜码、高频噪声解耦和帧间组合约束，端到端实现真实人脸视频的时间一致 GAN 反演与任意属性编辑。
摘要：
GAN 反演对于将 GAN 强大的可编辑性应用于真实图像不可或缺。然而，现有方法逐帧独立反演视频，往往会产生随时间变化、不一致且不理想的结果。本文提出一个统一的循环框架，名为 Recurrent vIdeo GAN Inversion and eDiting（RIGID），用于显式且同时地实现真实视频中时间一致的 GAN 反演与人脸编辑。该方法从三个方面建模当前帧与先前帧之间的时序关系。首先，为实现可信的真实视频重建，我们通过学习时间补偿潜码来最大化反演保真度与一致性。其次，我们观察到不一致噪声位于高频域，并且可从潜空间中解耦。第三，为消除属性操控后的不一致性，我们提出帧间组合约束，使任意帧都必须是其相邻帧的直接组合。该统一框架以端到端方式学习输入帧之间的内在一致性，因此不依赖于特定属性，无需重新训练即可应用于同一视频的任意编辑。大量实验表明，RIGID 在反演和编辑任务上的定性与定量表现均优于最先进方法。相关成果可参见 https://cnnlstm.github.io/RIGID。
英文摘要：
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}
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
