High-Fidelity GAN Inversion for Image Attribute Editing

Tengfei Wang, Yong Zhang, Yanbo Fan, Jue Wang, Qifeng Chen

Abstract: We present a novel highfidelity generative adversarial network (GAN) inversion framework that enables attribute editing with image-specific details well-preserved (e.g., background, appearance, and illumination). We first analyze the challenges of highfidelity GAN inversion from the perspective of lossy data compression. With a low bitrate latent code, previous works have difficulties in preserving highfidelity details in reconstructed and edited images. Increasing the size of a latent code can improve the accuracy of GAN inversion but at the cost of inferior editability. To improve image fidelity without compromising editability, we propose a distortion consultation approach that employs a distortion map as a reference for highfidelity reconstruction. In the distortion consultation inversion (DCI), the distortion map is first projected to a high-rate latent map, which then complements the basic low-rate latent code with more details via consultation fusion. To achieve high-fidelity editing, we propose an adaptive distortion alignment (ADA) module with a self-supervised training scheme, which bridges the gap between the edited and inversion images. Extensive experiments in the face and car domains show a clear improvement in both inversion and editing quality. The project page is https://tengfei-wang.github.io/HFGI/.