WINE : Wavelet-Guided GAN Inversion and Editing for High-Fidelity Refinement

Chaewon Kim, Seung Jun Moon, Gyeong-Moon Park; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 4523-4532

Recent advanced GAN inversion models aim to convey high-fidelity information from original images to generators through methods using generator tuning or high-dimensional feature learning. Despite these efforts accurately reconstructing image-specific details remains as a challenge due to the inherent limitations both in terms of training and structural aspects leading to a bias towards low-frequency information. In this paper we look into the widely used pixel loss in GAN inversion revealing its predominant focus on the reconstruction of low-frequency features. We then propose WINE a Wavelet-guided GAN inversion and editing model which transfers the high-frequency information through wavelet coefficients via newly proposed wavelet loss and wavelet fusion scheme. Notably WINE is the first attempt to interpret GAN inversion in the frequency domain. Our experimental results showcase the precision of WINE in preserving high-frequency details and enhancing image quality. Even in editing scenarios WINE outperforms existing state-of-the-art GAN inversion models with a fine balance between editability and reconstruction quality. Pre-trained model and codes will be publicized after the review process.