The fidelity of Generative Adversarial Networks (GAN) inversion is impeded by Out-Of-Domain (OOD) areas (e.g., background, accessories) in the image. Detecting the OOD areas beyond the generation ability of the pre-trained model and blending these regions with the input image can enhance fidelity. The "invertibility mask" figures out these OOD areas, and existing methods predict the mask with the reconstruction error. However, the estimated mask is usually inaccurate due to the influence of the reconstruction error in the In-Domain (ID) area. In this paper, we propose a novel framework that enhances the fidelity of human face inversion by designing a new module to decompose the input images to ID and OOD partitions with invertibility masks. Unlike previous works, our invertibility detector is simultaneously learned with a spatial alignment module. We iteratively align the generated features to the input geometry and reduce the reconstruction error in the ID regions. Thus, the OOD areas are more distinguishable and can be precisely predicted. Then, we improve the fidelity of our results by blending the OOD areas from the input image with the ID GAN inversion results. Our method produces photo-realistic results for real-world human face image inversion and manipulation. Extensive experiments demonstrate our method's superiority over existing methods in the quality of GAN inversion and attribute manipulation.

翻译：生成对抗网络（GAN）反演的保真度受限于图像中的域外（OOD）区域（如背景、配饰）。检测超出预训练模型生成能力的OOD区域，并将这些区域与输入图像进行融合，可提升保真度。“可逆性掩码”用于识别这些OOD区域，现有方法通过重构误差预测该掩码。然而，由于域内（ID）区域重构误差的影响，估算的掩码通常不准确。本文提出一种新颖框架，通过设计新模块将输入图像分解为ID和OOD两部分（利用可逆性掩码），从而提升人脸反演的保真度。与先前工作不同，我们的可逆性检测器与空间对齐模块联合学习，通过迭代对齐生成特征与输入几何结构，并降低ID区域的重构误差，使得OOD区域更易区分且能精确预测。随后，将输入图像的OOD区域与ID区域的GAN反演结果进行融合，进一步提升输出保真度。本方法可实现真实世界人脸图像的高保真反演与编辑。大量实验证明，本方法在GAN反演质量和属性编辑效果上均优于现有方法。