GAN-based image attribute editing firstly leverages GAN Inversion to project real images into the latent space of GAN and then manipulates corresponding latent codes. Recent inversion methods mainly utilize additional high-bit features to improve image details preservation, as low-bit codes cannot faithfully reconstruct source images, leading to the loss of details. However, during editing, existing works fail to accurately complement the lost details and suffer from poor editability. The main reason is they inject all the lost details indiscriminately at one time, which inherently induces the position and quantity of details to overfit source images, resulting in inconsistent content and artifacts in edited images. This work argues that details should be gradually injected into both the reconstruction and editing process in a multi-stage coarse-to-fine manner for better detail preservation and high editability. Therefore, a novel dual-stream framework is proposed to accurately complement details at each stage. The Reconstruction Stream is employed to embed coarse-to-fine lost details into residual features and then adaptively add them to the GAN generator. In the Editing Stream, residual features are accurately aligned by our Selective Attention mechanism and then injected into the editing process in a multi-stage manner. Extensive experiments have shown the superiority of our framework in both reconstruction accuracy and editing quality compared with existing methods.

翻译：基于GAN的图像属性编辑首先利用GAN逆映射将真实图像投影至GAN的隐空间，随后对相应隐编码进行操控。现有逆映射方法主要借助额外高位特征来提升图像细节保真度，因为低位编码无法忠实重建源图像会导致细节丢失。然而在编辑过程中，现有方法未能精确补充丢失的细节，且编辑能力较差。究其原因，在于它们将所有丢失细节不加区分地一次性注入，本质上导致了细节位置和数量对源图像的过拟合，进而造成编辑图像出现内容不一致与伪影。本文认为，应采用多阶段由粗到精的方式将细节逐步注入重建与编辑过程，以实现更优的细节保留与高编辑能力。为此，提出了一种新型双流框架以精确补充各阶段的细节。其中，重建流用于将由粗到精的丢失细节嵌入残差特征，并自适应地添加至GAN生成器；编辑流则通过所提出的选择性注意力机制精准对齐残差特征，并以多阶段方式注入编辑过程。大量实验表明，与现有方法相比，本框架在重建精度与编辑质量方面均具有显著优势。