Improving Detail in Pluralistic Image Inpainting with Feature Dequantization

Kyungri Park, Woohwan Jung; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 680-689

Pluralistic Image Inpainting (PII) offers multiple plausible solutions for restoring missing parts of images and has been successfully applied to various applications including image editing and object removal. Recently VQGAN-based methods have been proposed and have shown that they significantly improve the structural integrity in the generated images. Nevertheless the state-of-the-art VQGAN-based model PUT faces a critical challenge: degradation of detail quality in output images due to feature quantization. Feature quantization restricts the latent space and causes information loss which negatively affects the detail quality essential for image inpainting. To tackle the problem we propose the FDM (Feature Dequantization Module) specifically designed to restore the detail quality of images by compensating for the information loss. Furthermore we develop an efficient training method for FDM which drastically reduces training costs. We empirically demonstrate that our method significantly enhances the detail quality of the generated images with negligible training and inference overheads. The code is available at https://github.com/hyudsl/FDM