Numerous task-specific variants of conditional generative adversarial
networks have been developed for image completion. Yet, a serious limitation
remains that all existing algorithms tend to fail when handling large-scale
missing regions. To overcome this challenge, we propose a generic new approach
that bridges the gap between image-conditional and recent modulated
unconditional generative architectures via co-modulation of both conditional
and stochastic style representations. Also, due to the lack of good
quantitative metrics for image completion, we propose the new Paired/Unpaired
Inception Discriminative Score (P-IDS/U-IDS), which robustly measures the
perceptual fidelity of inpainted images compared to real images via linear
separability in a feature space. Experiments demonstrate superior performance
in terms of both quality and diversity over state-of-the-art methods in
free-form image completion and easy generalization to image-to-image
translation. Code is available at this https URL

提出了一种基于深度学习的图像补全方法，该方法通过融合条件生成和最近的无条件生成的模型结构，引入共同调制的方式来提高大规模缺失区域图像的补全质量，并使用新的 P-IDS/U-IDS 算法来提供真实图像的知觉保真度量化，综合实验数据表明，在自由形式图像补全和图像翻译方面，该方法的质量和多样性性能均优于现有方法。