# Bridging Global Context Interactions for High-Fidelity Image Completion

arXiv: https://arxiv.org/abs/2104.00845 · HTML: https://mlsanity.com/paper/2104.00845/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 64 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [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)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [Visual Transformers: Token-based Image Representation and Processing for Computer Vision](https://mlsanity.com/paper/2006.03677/) (2020)
- [Large Scale Image Completion via Co-Modulated Generative Adversarial Networks](https://mlsanity.com/paper/2103.10428/) (2021)
- [Pluralistic Image Completion](https://mlsanity.com/paper/1903.04227/) (2019)
- [High-Fidelity Pluralistic Image Completion with Transformers](https://mlsanity.com/paper/2103.14031/) (2021)
- [Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting](https://mlsanity.com/paper/2005.09704/) (2020)
- [Generative Adversarial Transformers](https://mlsanity.com/paper/2103.01209/) (2021)
- [SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color](https://mlsanity.com/paper/1902.06838/) (2019)

## Built on by (most cited first, 30 of 40)

- [Amodal3R: Amodal 3D Reconstruction from Occluded 2D Images](https://mlsanity.com/paper/2503.13439/) (2025)
- [PanoDiffusion: 360-degree Panorama Outpainting via Diffusion](https://mlsanity.com/paper/2307.03177/) (2023)
- [Deep Learning-based Image and Video Inpainting: A Survey](https://mlsanity.com/paper/2401.03395/) (2024)
- [ImageNet-E: Benchmarking Neural Network Robustness via Attribute Editing](https://mlsanity.com/paper/2303.17096/) (2023)
- [Keys to Better Image Inpainting: Structure and Texture Go Hand in Hand](https://mlsanity.com/paper/2208.03382/) (2022)
- [MaGIC: Multi-modality Guided Image Completion](https://mlsanity.com/paper/2305.11818/) (2023)
- [LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented Diffusion](https://mlsanity.com/paper/2404.00292/) (2024)
- [HINT: High-quality INPainting Transformer with Mask-Aware Encoding and Enhanced Attention](https://mlsanity.com/paper/2402.14185/) (2024)
- [VTAE: Variational Transformer Autoencoder with Manifolds Learning](https://mlsanity.com/paper/2304.00948/) (2023)
- [Consistent Image Layout Editing with Diffusion Models](https://mlsanity.com/paper/2503.06419/) (2025)
- [T-former: An Efficient Transformer for Image Inpainting](https://mlsanity.com/paper/2305.07239/) (2023)
- [Transformer based Pluralistic Image Completion with Reduced Information Loss](https://mlsanity.com/paper/2404.00513/) (2024)
- [Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting](https://mlsanity.com/paper/2403.18186/) (2024)
- [High-Quality Pluralistic Image Completion via Code Shared VQGAN](https://mlsanity.com/paper/2204.01931/) (2022)
- [Deficiency-Aware Masked Transformer for Video Inpainting](https://mlsanity.com/paper/2307.08629/) (2023)
- [CamFreeDiff: Camera-free Image to Panorama Generation with Diffusion Model](https://mlsanity.com/paper/2407.07174/) (2024)
- [GRIG: Few-Shot Generative Residual Image Inpainting](https://mlsanity.com/paper/2304.12035/) (2023)
- [Towards Interactive Image Inpainting via Sketch Refinement](https://mlsanity.com/paper/2306.00407/) (2023)
- [Foreground Focus: Enhancing Coherence and Fidelity in Camouflaged Image Generation](https://mlsanity.com/paper/2504.02180/) (2025)
- [Learning to Scale Temperature in Masked Self-Attention for Image Inpainting](https://mlsanity.com/paper/2302.06130/) (2023)
- [Text-guided Controllable Diffusion for Realistic Camouflage Images Generation](https://mlsanity.com/paper/2511.20218/) (2025)
- [Structure-Guided Image Completion with Image-level and Object-level Semantic Discriminators](https://mlsanity.com/paper/2212.06310/) (2022)
- [Thermal Infrared Image Inpainting via Edge-Aware Guidance](https://mlsanity.com/paper/2210.16000/) (2022)
- [DMAT: A Dynamic Mask-Aware Transformer for Human De-occlusion](https://mlsanity.com/paper/2402.04558/) (2024)
- [Gorgeous: Create Your Desired Character Facial Makeup from Any Ideas](https://mlsanity.com/paper/2404.13944/) (2024)
- [MxT: Mamba x Transformer for Image Inpainting](https://mlsanity.com/paper/2407.16126/) (2024)
- [SR-CurvANN: Advancing 3D Surface Reconstruction through Curvature-Aware Neural Networks](https://mlsanity.com/paper/2407.17896/) (2024)
- [RetCompletion:High-Speed Inference Image Completion with Retentive Network](https://mlsanity.com/paper/2410.04056/) (2024)
- [Binary Diffusion Probabilistic Model](https://mlsanity.com/paper/2501.13915/) (2025)
- [Frequency-Domain Fusion Transformer for Image Inpainting](https://mlsanity.com/paper/2506.18437/) (2025)

## Abstract (arXiv)

Bridging global context interactions correctly is important for high-fidelity image completion with large masks. Previous methods attempting this via deep or large receptive field (RF) convolutions cannot escape from the dominance of nearby interactions, which may be inferior. In this paper, we propose to treat image completion as a directionless sequence-to-sequence prediction task, and deploy a transformer to directly capture long-range dependence in the encoder. Crucially, we employ a restrictive CNN with small and non-overlapping RF for weighted token representation, which allows the transformer to explicitly model the long-range visible context relations with equal importance in all layers, without implicitly confounding neighboring tokens when larger RFs are used. To improve appearance consistency between visible and generated regions, a novel attention-aware layer (AAL) is introduced to better exploit distantly related high-frequency features. Overall, extensive experiments demonstrate superior performance compared to state-of-the-art methods on several datasets.