Image Inpainting via Conditional Texture and Structure Dual Generation

Abstract

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 https://github.com/Xiefan-Guo/CTSDG.

arXiv commentAccepted by ICCV 2021

References in corpus (6)

• Spectral Normalization for Generative Adversarial Networks
• Generative Image Inpainting with Contextual Attention
• StructureFlow: Image Inpainting via Structure-aware Appearance Flow
• Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting
• Foreground-aware Image Inpainting
• VCNet: A Robust Approach to Blind Image Inpainting

Cited by in corpus (1)

• Generative Image Inpainting with Segmentation Confusion Adversarial Training and Contrastive Learning