RIGID: Recurrent GAN Inversion and Editing of Real Face Videos

TL;DR

RIGID introduces a recurrent framework for temporally coherent GAN inversion and editing of real face videos, improving consistency and quality over existing methods without retraining for different edits.

Contribution

The paper proposes a novel end-to-end recurrent approach that models temporal relations for consistent video GAN inversion and editing, addressing issues of inconsistency and noise.

Findings

Outperforms state-of-the-art in inversion quality.

Achieves more consistent facial editing across frames.

Effective for arbitrary editing without retraining.

Abstract

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}