WINE: 用于高保真度优化的小波引导GAN反演与编辑
WINE: Wavelet-Guided GAN Inversion and Editing for High-Fidelity Refinement
AI总结：
针对现有GAN反演模型偏向低频信息、难以重建图像细节的问题，提出首个频域解读的WINE模型，通过小波损失与融合方案传递高频信息，在细节保留、图像质量及编辑场景的性能平衡上优于现有SOTA模型。
摘要：
近期的先进GAN反演模型旨在通过生成器调优或高维特征学习方法，将原始图像的高保真信息传递给生成器。尽管已有这些努力，由于训练和结构层面的固有局限性，准确重建图像专属细节仍然是一项挑战，导致模型偏向低频信息。本文研究了GAN反演中广泛使用的像素损失，揭示其主要聚焦于低频特征的重建。随后我们提出WINE（小波引导的GAN反演与编辑模型），该模型通过新提出的小波损失和小波融合方案，利用小波系数传递高频信息。值得注意的是，WINE是首个在频域中解读GAN反演的尝试。我们的实验结果展示了WINE在保留高频细节和提升图像质量方面的精准性。即使在编辑场景下，WINE也在可编辑性与重建质量之间实现了良好平衡，性能优于现有的最先进GAN反演模型。
英文摘要：
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.
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。
