# PiiGAN: Generative Adversarial Networks for Pluralistic Image Inpainting

arXiv: https://arxiv.org/abs/1912.01834 · HTML: https://mlsanity.com/paper/1912.01834/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2019.

## Built on by (most cited first, 17 of 17)

- [A Complete Survey on Generative AI (AIGC): Is ChatGPT from GPT-4 to GPT-5 All You Need?](https://mlsanity.com/paper/2303.11717/) (2023)
- [TrajeVAE: Controllable Human Motion Generation from Trajectories](https://mlsanity.com/paper/2104.00351/) (2021)
- [A Probabilistic Hard Attention Model For Sequentially Observed Scenes](https://mlsanity.com/paper/2111.07534/) (2021)
- [From Posterior Sampling to Meaningful Diversity in Image Restoration](https://mlsanity.com/paper/2310.16047/) (2023)
- [FT-TDR: Frequency-guided Transformer and Top-Down Refinement Network for Blind Face Inpainting](https://mlsanity.com/paper/2108.04424/) (2021)
- [A Survey of Generative AI for Intelligent Transportation Systems: Road Transportation Perspective](https://mlsanity.com/paper/2312.08248/) (2023)
- [Image Inpainting Using Wasserstein Generative Adversarial Imputation Network](https://mlsanity.com/paper/2106.15341/) (2021)
- [Predictive World Models from Real-World Partial Observations](https://mlsanity.com/paper/2301.04783/) (2023)
- [Internal Diverse Image Completion](https://mlsanity.com/paper/2212.10280/) (2022)
- [An Analysis of Generative Methods for Multiple Image Inpainting](https://mlsanity.com/paper/2205.02146/) (2022)
- [Line Drawing Guided Progressive Inpainting of Mural Damage](https://mlsanity.com/paper/2211.06649/) (2022)
- [E2F-Net: Eyes-to-Face Inpainting via StyleGAN Latent Space](https://mlsanity.com/paper/2403.12197/) (2024)
- [Diverse Image Harmonization](https://mlsanity.com/paper/2407.15481/) (2024)
- [Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks](https://mlsanity.com/paper/2101.01073/) (2021)
- [Reference Guided Image Inpainting using Facial Attributes](https://mlsanity.com/paper/2301.08044/) (2023)
- [MGAN-CRCM: A Novel Multiple Generative Adversarial Network and Coarse-Refinement Based Cognizant Method for Image Inpainting](https://mlsanity.com/paper/2412.19000/) (2024)
- [Reducing the gap between general purpose data and aerial images in concentrated solar power plants](https://mlsanity.com/paper/2508.00440/) (2025)

## Abstract (arXiv)

The latest methods based on deep learning have achieved amazing results regarding the complex work of inpainting large missing areas in an image. But this type of method generally attempts to generate one single "optimal" result, ignoring many other plausible results. Considering the uncertainty of the inpainting task, one sole result can hardly be regarded as a desired regeneration of the missing area. In view of this weakness, which is related to the design of the previous algorithms, we propose a novel deep generative model equipped with a brand new style extractor which can extract the style feature (latent vector) from the ground truth. Once obtained, the extracted style feature and the ground truth are both input into the generator. We also craft a consistency loss that guides the generated image to approximate the ground truth. After iterations, our generator is able to learn the mapping of styles corresponding to multiple sets of vectors. The proposed model can generate a large number of results consistent with the context semantics of the image. Moreover, we evaluated the effectiveness of our model on three datasets, i.e., CelebA, PlantVillage, and MauFlex. Compared to state-of-the-art inpainting methods, this model is able to offer desirable inpainting results with both better quality and higher diversity. The code and model will be made available on https://github.com/vivitsai/PiiGAN.