SwapInpaint2: Towards high structural consistency in identity-guided inpainting via background-preserving GAN inversion

In this work, we propose SwapInpaint2 to enhance the naturalness of identity-guided face inpainting. The previous version, SwapInpaint, relied on a generic inpainting model for content infer, bringing the problems of unnatural structure to the results. Our method addresses this issue by utilizing an inversion-based Background- preserving Attribute Extractor and an improved Embedding Integration Generator to provide high-quality attribute embeddings and produce high-naturalistic results. Comparison experiments with state-of-the-art models demonstrate that our new method achieves superior structural consistency and stronger style alignment both in terms of quality and quantity.