Abstract
Generative Adversarial Network (GAN) inversion have demonstrated excellent performance in image inpainting that aims to restore lost or damaged image texture using its unmasked content. Previous GAN inversion-based methods usually utilize well-trained GAN models as effective priors to generate the realistic regions for missing holes. Despite excellence, they ignore a hard constraint that the unmasked regions in the input and the output should be the same, resulting in a gap between GAN inversion and image inpainting and thus degrading the performance. Besides, existing GAN inversion approaches often consider a single modality of the input image, neglecting other auxiliary cues in images for improvements. Addressing these problems, we propose a novel GAN inversion approach, dubbed MMInvertFill, for image inpainting. MMInvertFill contains primarily a multimodal guided encoder with a pre-modulation and a GAN generator with F&W+ latent space. Specifically, the multimodal encoder aims to enhance the multi-scale structures with additional semantic segmentation edge texture modalities through a gated mask-aware attention module. Afterwards, a pre-modulation is presented to encode these structures into style vectors. To mitigate issues of conspicuous color discrepancy and semantic inconsistency, we introduce the F&W+ latent space to bridge the gap between GAN inversion and image inpainting. Furthermore, in order to reconstruct faithful and photorealistic images, we devise a simple yet effective Soft-update Mean Latent module to capture more diversified in-domain patterns for generating high-fidelity textures for massive corruptions. In our extensive experiments on six challenging datasets, we show that our MMInvertFill qualitatively and quantitatively outperforms other state-of-the-arts and it supports the completion of out-of-domain images effectively.
Abstract (translated)
生成对抗网络（GAN）逆向技术在图像修复领域中展示了卓越的性能，其目标是利用未遮挡的内容恢复丢失或损坏的纹理。之前的基于 GAN 逆向的方法通常会使用经过良好训练的 GAN 模型作为有效的先验条件来生成缺失区域的真实感部分。尽管这些方法表现优秀，但它们忽略了输入图像和输出图像中未遮挡区域应当保持一致这一严格的约束条件，这导致了 GAN 逆向与图像修复之间的差距，并因此降低了性能。此外，现有的 GAN 逆向方法通常仅考虑输入图像的单一模式，忽视了其他在改进方面有帮助的辅助线索。 为了应对这些挑战，我们提出了一种新颖的 GAN 逆向方法，称为 MMInvertFill，用于图像修复。MMInvertFill 主要包含一个多模态引导编码器和一个 F&W+ 潜空间中的 GAN 发生器。具体来说，多模态编码器旨在通过门控掩码感知注意模块增强多层次结构，并且引入了额外的语义分割边缘纹理模式。随后，我们提出预调制技术将这些结构编码为样式向量。为了缓解明显的颜色差异和语义不一致的问题，我们引进 F&W+ 潜空间来弥合 GAN 逆向与图像修复之间的差距。 进一步地，为了重构忠实且逼真的图像，我们设计了一个简单而有效的软更新均值潜空间模块，以捕捉更多的域内模式，并为大量损坏生成高质量的纹理。在六个具有挑战性的数据集上的广泛实验中，我们的 MMInvertFill 从定性和定量上都超越了当前最佳方法，并支持跨域图像的有效完成任务。
URL
https://arxiv.org/abs/2504.12844
