渐进残差对齐：一种用于GAN反演与图像属性编辑的双流框架
Gradual Residuals Alignment: A Dual-Stream Framework for GAN Inversion and Image Attribute Editing
AI总结：
本文提出一种双流框架，通过多阶段由粗到细的方式逐步注入残差特征，并结合选择性注意力机制，解决GAN反演与属性编辑中细节丢失和可编辑性差的问题，在重建精度和编辑质量上优于现有方法。
摘要：
基于GAN的图像属性编辑首先利用GAN反演将真实图像投影到GAN的潜在空间中，然后操纵相应的潜在编码。近期的反演方法主要利用额外的高比特特征来改善图像细节保留，因为低比特编码无法忠实地重建源图像，导致细节丢失。然而，在编辑过程中，现有工作未能准确补充丢失的细节，并且可编辑性较差。主要原因是它们一次性不加区分地注入所有丢失的细节，这本质上导致细节的位置和数量过度拟合源图像，从而在编辑后的图像中产生不一致的内容和伪影。本文认为，细节应以多阶段由粗到细的方式逐步注入重建和编辑过程，以实现更好的细节保留和高可编辑性。因此，提出了一种新颖的双流框架，以在每个阶段准确补充细节。重建流用于将由粗到细的丢失细节嵌入残差特征中，然后自适应地将它们添加到GAN生成器中。在编辑流中，残差特征通过我们的选择性注意力机制被准确对齐，然后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，我们的框架在重建精度和编辑质量方面均具有优越性。
英文摘要：
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.
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。
