# LaFIn: Generative Landmark Guided Face Inpainting

arXiv: https://arxiv.org/abs/1911.11394 · HTML: https://mlsanity.com/paper/1911.11394/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 12 of 12)

- [Large Scale Image Completion via Co-Modulated Generative Adversarial Networks](https://mlsanity.com/paper/2103.10428/) (2021)
- [M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection](https://mlsanity.com/paper/2104.09770/) (2021)
- [FT-TDR: Frequency-guided Transformer and Top-Down Refinement Network for Blind Face Inpainting](https://mlsanity.com/paper/2108.04424/) (2021)
- [Deep Face Video Inpainting via UV Mapping](https://mlsanity.com/paper/2109.00681/) (2021)
- [Attention based Occlusion Removal for Hybrid Telepresence Systems](https://mlsanity.com/paper/2112.01098/) (2021)
- [A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip](https://mlsanity.com/paper/2208.01149/) (2022)
- [INCLG: Inpainting for Non-Cleft Lip Generation with a Multi-Task Image Processing Network](https://mlsanity.com/paper/2305.10589/) (2023)
- [E2F-Net: Eyes-to-Face Inpainting via StyleGAN Latent Space](https://mlsanity.com/paper/2403.12197/) (2024)
- [V-LinkNet: Learning Contextual Inpainting Across Latent Space of Generative Adversarial Network](https://mlsanity.com/paper/2201.00323/) (2022)
- [Non-Deterministic Face Mask Removal Based On 3D Priors](https://mlsanity.com/paper/2202.09856/) (2022)
- [Wavelet Prior Attention Learning in Axial Inpainting Network](https://mlsanity.com/paper/2206.03113/) (2022)
- [Facial De-occlusion Network for Virtual Telepresence Systems](https://mlsanity.com/paper/2210.12622/) (2022)

## Abstract (arXiv)

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.