Neural Image Inpainting Guided with Descriptive Text

Neural image inpainting has achieved promising performance in generating semantically plausible content. Most of the recent works mainly focus on inpainting images depending on vision information, while neglecting the semantic information implied in human languages. To acquire more semantically accurate inpainting images, this paper proposes a novel inpainting model named \textit{N}eural \textit{I}mage Inpainting \textit{G}uided with \textit{D}escriptive \textit{T}ext (NIGDT). First, a dual multi-modal attention mechanism is designed to extract the explicit semantic information about corrupted regions. The mechanism is trained to combine the descriptive text and two complementary images through reciprocal attention maps. Second, an image-text matching loss is designed to enforce the model output following the descriptive text. Its goal is to maximize the semantic similarity of the generated image and the text. Finally, experiments are conducted on two open datasets with captions. Experimental results show that the proposed NIGDT model outperforms all compared models on both quantitative and qualitative comparison. The results also demonstrate that the proposed model can generate images consistent with the guidance text, which provides a flexible way for user-guided inpainting. Our systems and code will be released soon.