Learning From Text: A Multimodal Face Inpainting Network for Irregular Holes

Irregular hole face inpainting is a challenging task, since the appearance of faces varies greatly (e.g., different expressions and poses) and the human vision is more sensitive to subtle blemishes in the inpainted face images. Without external information, most existing methods struggle to generate new content containing semantic information for face components in the absence of sufficient contextual information. As it is known that text can be used to describe the content of an image in most cases, and is flexible and user-friendly. In this work, a concise and effective Multimodal Face Inpainting Network (MuFIN) is proposed, which simultaneously utilizes the information of the known regions and the descriptive text of the input image to address the problem of irregular hole face inpainting. To fully exploit the rest parts of the corrupted face images, a plug-and-play Multi-scale Multi-level Skip Fusion Module (MMSFM), which extracts multi-scale features and fuses shallow features into deep features at multiple levels, is illustrated. Moreover, to bridge the gap between textual and visual modalities and effectively fuse cross-modal features, a Multi-scale Text-Image Fusion Block (MTIFB), which incorporates text features into image features from both local and global scales, is developed. Extensive experiments conducted on two commonly used datasets CelebA and Multi-Modal-CelebA-HQ demonstrate that our method outperforms state-of-the-art methods both qualitatively and quantitatively, and can generate realistic and controllable results.