LaFIn: Generative Landmark Guided Face Inpainting

TL;DR

This paper introduces LaFIn, a face inpainting method that combines landmark prediction and appearance generation to produce realistic, attribute-consistent face images in challenging wild scenarios, outperforming existing methods.

Contribution

The paper proposes a novel deep learning framework integrating landmark prediction with face inpainting, enhancing realism and attribute consistency in wild face inpainting tasks.

Findings

Outperforms state-of-the-art methods qualitatively and quantitatively

Effective in preserving facial structure and attributes

Augmented data improves landmark prediction accuracy

Abstract

It is challenging to inpaint face images in the wild, due to the large variation of appearance, such as different poses, expressions and occlusions. A good inpainting algorithm should guarantee the realism of output, including the topological structure among eyes, nose and mouth, as well as the attribute consistency on pose, gender, ethnicity, expression, etc. This paper studies an effective deep learning based strategy to deal with these issues, which comprises of a facial landmark predicting subnet and an image inpainting subnet. Concretely, given partial observation, the landmark predictor aims to provide the structural information (e.g. topological relationship and expression) of incomplete faces, while the inpaintor is to generate plausible appearance (e.g. gender and ethnicity) conditioned on the predicted landmarks. Experiments on the CelebA-HQ and CelebA datasets are conducted to reveal the efficacy of our design and, to demonstrate its superiority over state-of-the-art alternatives both qualitatively and quantitatively. In addition, we assume that high-quality completed faces together with their landmarks can be utilized as augmented data to further improve the performance of (any) landmark predictor, which is corroborated by experimental results on the 300W and WFLW datasets.