# ReGO: Reference-Guided Outpainting for Scenery Image

arXiv: https://arxiv.org/abs/2106.10601 · HTML: https://mlsanity.com/paper/2106.10601/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 62 references that are in the map.
- First posted on arXiv 20 Jun 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Improved Techniques for Training GANs](https://mlsanity.com/paper/1606.03498/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Holistically-Nested Edge Detection](https://mlsanity.com/paper/1504.06375/) (2015)
- [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)
- [Large Scale Image Completion via Co-Modulated Generative Adversarial Networks](https://mlsanity.com/paper/2103.10428/) (2021)
- [Deep Photo Style Transfer](https://mlsanity.com/paper/1703.07511/) (2017)
- [High-Fidelity Pluralistic Image Completion with Transformers](https://mlsanity.com/paper/2103.14031/) (2021)
- [StructureFlow: Image Inpainting via Structure-aware Appearance Flow](https://mlsanity.com/paper/1908.03852/) (2019)
- [High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis](https://mlsanity.com/paper/1611.09969/) (2016)
- [SketchyGAN: Towards Diverse and Realistic Sketch to Image Synthesis](https://mlsanity.com/paper/1801.02753/) (2018)
- [Image Inpainting with Learnable Bidirectional Attention Maps](https://mlsanity.com/paper/1909.00968/) (2019)
- [Boundless: Generative Adversarial Networks for Image Extension](https://mlsanity.com/paper/1908.07007/) (2019)
- [In&Out : Diverse Image Outpainting via GAN Inversion](https://mlsanity.com/paper/2104.00675/) (2021)
- [Very Long Natural Scenery Image Prediction by Outpainting](https://mlsanity.com/paper/1912.12688/) (2019)
- [TransFill: Reference-guided Image Inpainting by Merging Multiple Color and Spatial Transformations](https://mlsanity.com/paper/2103.15982/) (2021)
- [Attention-aware Multi-stroke Style Transfer](https://mlsanity.com/paper/1901.05127/) (2019)
- [Image Inpainting by End-to-End Cascaded Refinement with Mask Awareness](https://mlsanity.com/paper/2104.13743/) (2021)
- [Compatible and Diverse Fashion Image Inpainting](https://mlsanity.com/paper/1902.01096/) (2019)
- [Progressive Image Inpainting with Full-Resolution Residual Network](https://mlsanity.com/paper/1907.10478/) (2019)
- [Controllable and Progressive Image Extrapolation](https://mlsanity.com/paper/1912.11711/) (2019)
- [Sketch-Guided Scenery Image Outpainting](https://mlsanity.com/paper/2006.09788/) (2020)

## Built on by (most cited first, 6 of 6)

- [DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology](https://mlsanity.com/paper/2306.13384/) (2023)
- [EditTransfer++: Toward Faithful and Efficient Visual-Prompt-Guided Image Editing](https://mlsanity.com/paper/2605.07455/) (2026)
- [Image Protection for Robust Cropping Localization and Recovery](https://mlsanity.com/paper/2206.02405/) (2022)
- [Generative Adversarial Networks in Ultrasound Imaging: Extending Field of View Beyond Conventional Limits](https://mlsanity.com/paper/2405.20981/) (2024)
- [Time-variant Image Inpainting via Interactive Distribution Transition Estimation](https://mlsanity.com/paper/2506.23461/) (2025)
- [High-Resolution Artwork Outpainting with Global Blueprint Guidance and Layout Control](https://mlsanity.com/paper/2607.06162/) (2026)

## Abstract (arXiv)

We aim to tackle the challenging yet practical scenery image outpainting task in this work. Recently, generative adversarial learning has significantly advanced the image outpainting by producing semantic consistent content for the given image. However, the existing methods always suffer from the blurry texture and the artifacts of the generative part, making the overall outpainting results lack authenticity. To overcome the weakness, this work investigates a principle way to synthesize texture-rich results by borrowing pixels from its neighbors (i.e., reference images), named Reference-Guided Outpainting (ReGO). Particularly, the ReGO designs an Adaptive Content Selection (ACS) module to transfer the pixel of reference images for texture compensating of the target one. To prevent the style of the generated part from being affected by the reference images, a style ranking loss is further proposed to augment the ReGO to synthesize style-consistent results. Extensive experiments on two popular benchmarks, NS6K citeyangzx and NS8K citewang, well demonstrate the effectiveness of our ReGO. Our code will be made public available.