# Controllable and Progressive Image Extrapolation

arXiv: https://arxiv.org/abs/1912.11711 · HTML: https://mlsanity.com/paper/1912.11711/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2019.

## Built on by (most cited first, 13 of 13)

- [InsetGAN for Full-Body Image Generation](https://mlsanity.com/paper/2203.07293/) (2022)
- [Amodal Completion via Progressive Mixed Context Diffusion](https://mlsanity.com/paper/2312.15540/) (2023)
- [Understanding the Role of Scene Graphs in Visual Question Answering](https://mlsanity.com/paper/2101.05479/) (2021)
- [GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial Networks](https://mlsanity.com/paper/2101.09978/) (2021)
- [Cylin-Painting: Seamless 360textdegree Panoramic Image Outpainting and Beyond](https://mlsanity.com/paper/2204.08563/) (2022)
- [Memory-Driven Text-to-Image Generation](https://mlsanity.com/paper/2208.07022/) (2022)
- [ReGO: Reference-Guided Outpainting for Scenery Image](https://mlsanity.com/paper/2106.10601/) (2021)
- [LatentKeypointGAN: Controlling Images via Latent Keypoints](https://mlsanity.com/paper/2103.15812/) (2021)
- [Controllable Image Generation via Collage Representations](https://mlsanity.com/paper/2304.13722/) (2023)
- [Synthesizing Environment-Specific People in Photographs](https://mlsanity.com/paper/2312.14579/) (2023)
- [Graph Community Augmentation with GMM-based Modeling in Latent Space](https://mlsanity.com/paper/2412.01163/) (2024)
- [LatentKeypointGAN: Controlling Images via Latent Keypoints -- Extended Abstract](https://mlsanity.com/paper/2205.03448/) (2022)
- [High-Resolution Artwork Outpainting with Global Blueprint Guidance and Layout Control](https://mlsanity.com/paper/2607.06162/) (2026)

## Abstract (arXiv)

Image extrapolation aims at expanding the narrow field of view of a given image patch. Existing models mainly deal with natural scene images of homogeneous regions and have no control of the content generation process. In this work, we study conditional image extrapolation to synthesize new images guided by the input structured text. The text is represented as a graph to specify the objects and their spatial relation to the unknown regions of the image. Inspired by drawing techniques, we propose a progressive generative model of three stages, i.e., generating a coarse bounding-boxes layout, refining it to a finer segmentation layout, and mapping the layout to a realistic output. Such a multi-stage design is shown to facilitate the training process and generate more controllable results. We validate the effectiveness of the proposed method on the face and human clothing dataset in terms of visual results, quantitative evaluations and flexible controls.