# Foreground-guided Facial Inpainting with Fidelity Preservation

arXiv: https://arxiv.org/abs/2105.03342 · HTML: https://mlsanity.com/paper/2105.03342/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 20 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Foreground-aware Image Inpainting](https://mlsanity.com/paper/1901.05945/) (2019)
- [Image inpainting: A review](https://mlsanity.com/paper/1909.06399/) (2019)
- [Semantic Image Segmentation via Deep Parsing Network](https://mlsanity.com/paper/1509.02634/) (2015)
- [Learning to Look around Objects for Top-View Representations of Outdoor Scenes](https://mlsanity.com/paper/1803.10870/) (2018)
- [R-MNet: A Perceptual Adversarial Network for Image Inpainting](https://mlsanity.com/paper/2008.04621/) (2020)
- [Semi-parametric Image Inpainting](https://mlsanity.com/paper/1807.02855/) (2018)
- [Semantic Foreground Inpainting from Weak Supervision](https://mlsanity.com/paper/1909.04564/) (2019)
- [Symmetric Skip Connection Wasserstein GAN for High-Resolution Facial Image Inpainting](https://mlsanity.com/paper/2001.03725/) (2020)

## Built on by (most cited first, 1 of 1)

- [V-LinkNet: Learning Contextual Inpainting Across Latent Space of Generative Adversarial Network](https://mlsanity.com/paper/2201.00323/) (2022)

## Abstract (arXiv)

Facial image inpainting, with high-fidelity preservation for image realism, is a very challenging task. This is due to the subtle texture in key facial features (component) that are not easily transferable. Many image inpainting techniques have been proposed with outstanding capabilities and high quantitative performances recorded. However, with facial inpainting, the features are more conspicuous and the visual quality of the blended inpainted regions are more important qualitatively. Based on these facts, we design a foreground-guided facial inpainting framework that can extract and generate facial features using convolutional neural network layers. It introduces the use of foreground segmentation masks to preserve the fidelity. Specifically, we propose a new loss function with semantic capability reasoning of facial expressions, natural and unnatural features (make-up). We conduct our experiments using the CelebA-HQ dataset, segmentation masks from CelebAMask-HQ (for foreground guidance) and Quick Draw Mask (for missing regions). Our proposed method achieved comparable quantitative results when compare to the state of the art but qualitatively, it demonstrated high-fidelity preservation of facial components.