Recently, inversion methods have focused on additional high-rate information in the generator (e.g., weights or intermediate features) to refine inversion and editing results from embedded latent codes. Although these techniques gain reasonable improvement in reconstruction, they decrease editing capability, especially on complex images (e.g., containing occlusions, detailed backgrounds, and artifacts). A vital crux is refining inversion results, avoiding editing capability degradation. To tackle this problem, we introduce Domain-Specific Hybrid Refinement (DHR), which draws on the advantages and disadvantages of two mainstream refinement techniques to maintain editing ability with fidelity improvement. Specifically, we first propose Domain-Specific Segmentation to segment images into two parts: in-domain and out-of-domain parts. The refinement process aims to maintain the editability for in-domain areas and improve two domains' fidelity. We refine these two parts by weight modulation and feature modulation, which we call Hybrid Modulation Refinement. Our proposed method is compatible with all latent code embedding methods. Extension experiments demonstrate that our approach achieves state-of-the-art in real image inversion and editing. Code is available at https://github.com/caopulan/GANInverter/tree/main/configs/dhr.

翻译：近年来，反演方法主要关注生成器中的高阶信息（例如权重或中间特征），以优化从嵌入潜码得到的反演与编辑结果。尽管这些技术在重建方面取得了合理改进，但它们会削弱编辑能力，尤其是在复杂图像（例如包含遮挡、精细背景和伪影）上。关键在于改进反演结果时避免编辑能力下降。为解决这一问题，我们提出了领域特定混合优化（DHR），该方法融合了两种主流优化技术的优缺点，在保持编辑能力的同时提升保真度。具体地，我们首先提出领域特定分割，将图像划分为两部分：域内部分和域外部分。优化过程旨在维持域内区域的可编辑性并提升两个域的保真度。我们通过权重调制和特征调制来优化这两部分，称之为混合调制优化。所提出的方法与所有潜码嵌入方法兼容。扩展实验表明，我们的方法在真实图像反演与编辑中达到了最先进水平。代码位于 https://github.com/caopulan/GANInverter/tree/main/configs/dhr。