# Guidance and Evaluation: Semantic-Aware Image Inpainting for Mixed Scenes

arXiv: https://arxiv.org/abs/2003.06877 · HTML: https://mlsanity.com/paper/2003.06877/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2020.

## Built on by (most cited first, 30 of 43)

- [Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE](https://mlsanity.com/paper/2103.10022/) (2021)
- [Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding](https://mlsanity.com/paper/2203.00867/) (2022)
- [Diverse Image Inpainting with Bidirectional and Autoregressive Transformers](https://mlsanity.com/paper/2104.12335/) (2021)
- [Image Inpainting via Conditional Texture and Structure Dual Generation](https://mlsanity.com/paper/2108.09760/) (2021)
- [TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations](https://mlsanity.com/paper/2103.15982/) (2021)
- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches](https://mlsanity.com/paper/2111.15078/) (2021)
- [DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology](https://mlsanity.com/paper/2306.13384/) (2023)
- [Learning a Sketch Tensor Space for Image Inpainting of Man-made Scenes](https://mlsanity.com/paper/2103.15087/) (2021)
- [Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment](https://mlsanity.com/paper/2207.03723/) (2022)
- [Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand](https://mlsanity.com/paper/2208.03382/) (2022)
- [ZITS++: Image Inpainting by Improving the Incremental Transformer on Structural Priors](https://mlsanity.com/paper/2210.05950/) (2022)
- [Towards Enhanced Image Inpainting: Mitigating Unwanted Object Insertion and Preserving Color Consistency](https://mlsanity.com/paper/2312.04831/) (2023)
- [AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints](https://mlsanity.com/paper/2205.10636/) (2022)
- [Gradpaint: Gradient-Guided Inpainting with Diffusion Models](https://mlsanity.com/paper/2309.09614/) (2023)
- [Learning Prior Feature and Attention Enhanced Image Inpainting](https://mlsanity.com/paper/2208.01837/) (2022)
- [MaGIC: Multi-modality Guided Image Completion](https://mlsanity.com/paper/2305.11818/) (2023)
- [Delving Globally into Texture and Structure for Image Inpainting](https://mlsanity.com/paper/2209.08217/) (2022)
- [Unbiased Multi-Modality Guidance for Image Inpainting](https://mlsanity.com/paper/2208.11844/) (2022)
- [Context-Aware Image Inpainting with Learned Semantic Priors](https://mlsanity.com/paper/2106.07220/) (2021)
- [GeoFill: Reference-Based Image Inpainting with Better Geometric Understanding](https://mlsanity.com/paper/2201.08131/) (2022)
- [Unsupervised Foggy Scene Understanding via Self Spatial-Temporal Label Diffusion](https://mlsanity.com/paper/2206.04879/) (2022)
- [T-former: An Efficient Transformer for Image Inpainting](https://mlsanity.com/paper/2305.07239/) (2023)
- [Reference-Guided Texture and Structure Inference for Image Inpainting](https://mlsanity.com/paper/2207.14498/) (2022)
- [TransRef: Multi-Scale Reference Embedding Transformer for Reference-Guided Image Inpainting](https://mlsanity.com/paper/2306.11528/) (2023)
- [Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid](https://mlsanity.com/paper/2112.04107/) (2021)
- [Reference-based Painterly Inpainting via Diffusion: Crossing the Wild Reference Domain Gap](https://mlsanity.com/paper/2307.10584/) (2023)
- [Interactive Image Inpainting Using Semantic Guidance](https://mlsanity.com/paper/2201.10753/) (2022)
- [CaRaFFusion: Improving 2D Semantic Segmentation with Camera-Radar Point Cloud Fusion and Zero-Shot Image Inpainting](https://mlsanity.com/paper/2505.03679/) (2025)
- [Structure First Detail Next: Image Inpainting with Pyramid Generator](https://mlsanity.com/paper/2106.08905/) (2021)

## Abstract (arXiv)

Completing a corrupted image with correct structures and reasonable textures for a mixed scene remains an elusive challenge. Since the missing hole in a mixed scene of a corrupted image often contains various semantic information, conventional two-stage approaches utilizing structural information often lead to the problem of unreliable structural prediction and ambiguous image texture generation. In this paper, we propose a Semantic Guidance and Evaluation Network (SGE-Net) to iteratively update the structural priors and the inpainted image in an interplay framework of semantics extraction and image inpainting. It utilizes semantic segmentation map as guidance in each scale of inpainting, under which location-dependent inferences are re-evaluated, and, accordingly, poorly-inferred regions are refined in subsequent scales. Extensive experiments on real-world images of mixed scenes demonstrated the superiority of our proposed method over state-of-the-art approaches, in terms of clear boundaries and photo-realistic textures.