Diverse Image Inpainting with Normalizing Flow
Cairong Wang, Yiming Zhu, Chun Yuan
"Image Inpainting is an ill-posed problem since there are diverse possible counterparts for the missing areas. The challenge of inpainting is to keep the ""corrupted region"" content consistent with the background and generate a variety of reasonable texture details. However, existing one-stage methods that directly output the inpainting results have to make a trade-off between diversity and consistency. The two-stage methods as the current trend can circumvent such shortcomings. These methods predict diverse structural priors in the first stage and focus on rich texture details generation in the second stage. However, all two-stage methods require autoregressive models to predict the probability distribution of the structural priors, which significantly limits the inference speed. In addition, their discretization assumption of prior distribution reduces the diversity of the inpainting results. We propose Flow-Fill, a novel two-stage image inpainting framework that utilizes a conditional normalizing flow model to generate diverse structural priors in the first stage. Flow-Fill can directly estimate the joint probability density of the missing regions as a flow-based model without reasoning pixel by pixel. Hence it achieves real-time inference speed and eliminates discretization assumptions. In addition, as a reversible model, Flow-Fill can invert the latent variables for a specified region, which allows us to make the inference process as semantic image editing. Experiments on benchmark datasets validate that Flow-Fill achieves superior diversity and fidelity in image inpainting qualitatively and quantitatively."