Toward Interactive Image Inpainting via Robust Sketch Refinement

Chang Liu, Shunxin Xu, Jialun Peng, Kaidong Zhang, Dong Liu

Abstract: One tough problem of image inpainting is to restore complex structures in the corrupted regions. It motivates interactive image inpainting which leverages additional hints, e.g., sketches, to assist the inpainting process. A sketch is simple and intuitive for end users to provide, but meanwhile has free forms with much randomness. Such randomness may confuse the inpainting models, and incur severe artifacts in completed images. To better facilitate image inpainting with sketch guidance, we propose a two-stage image inpainting system, termed SketchRefiner. The first stage of our approach serves as a data provider that simulates real sketches and derives the capability of sketch calibration from the simulated data. In the second stage, our approach aligns the sketch guidance with the inpainting process so as to elevate image inpainting with sketches. We also propose a real-world test protocol to address the evaluation of inpainting methods upon practical applications with user sketches. Experimental results on three prevailing benchmark datasets, i.e., CelebA-HQ, Places2, and ImageNet, and the proposed test protocol demonstrate the state-of-the-art performance of our approach, and its great potentials upon real-world applications. Further analyses illustrate that our approach effectively utilizes sketch information as guidance and eliminates the artifacts due to the free-form sketches.