Deep generative approaches have recently made considerable progress in image
inpainting by introducing structure priors. Due to the lack of proper
interaction with image texture during structure reconstruction, however,
current solutions are incompetent in handling the cases with large corruptions,
and they generally suffer from distorted results. In this paper, we propose a
novel two-stream network for image inpainting, which models the
structure-constrained texture synthesis and texture-guided structure
reconstruction in a coupled manner so that they better leverage each other for
more plausible generation. Furthermore, to enhance the global consistency, a
Bi-directional Gated Feature Fusion (Bi-GFF) module is designed to exchange and
combine the structure and texture information and a Contextual Feature
Aggregation (CFA) module is developed to refine the generated contents by
region affinity learning and multi-scale feature aggregation. Qualitative and
quantitative experiments on the CelebA, Paris StreetView and Places2 datasets
demonstrate the superiority of the proposed method. Our code is available at
this https URL

该研究提出了一种新的双流网络方法，能够模拟结构约束纹理合成和纹理引导结构重构，并且通过 Bi-directional Gated Feature Fusion 模块和 Contextual Feature Aggregation 模块实现更好的全局一致性和多尺度特征聚合，定量和定性实验证明了该方法的优越性。