# 180-degree Outpainting from a Single Image

arXiv: https://arxiv.org/abs/2001.04568 · HTML: https://mlsanity.com/paper/2001.04568/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 3 of 3)

- [Guided Co-Modulated GAN for 360deg Field of View Extrapolation](https://mlsanity.com/paper/2204.07286/) (2022)
- [Scene Graph Expansion for Semantics-Guided Image Outpainting](https://mlsanity.com/paper/2205.02958/) (2022)
- [CamFreeDiff: Camera-free Image to Panorama Generation with Diffusion Model](https://mlsanity.com/paper/2407.07174/) (2024)

## Abstract (arXiv)

Presenting context images to a viewer's peripheral vision is one of the most effective techniques to enhance immersive visual experiences. However, most images only present a narrow view, since the field-of-view (FoV) of standard cameras is small. To overcome this limitation, we propose a deep learning approach that learns to predict a 180deg panoramic image from a narrow-view image. Specifically, we design a foveated framework that applies different strategies on near-periphery and mid-periphery regions. Two networks are trained separately, and then are employed jointly to sequentially perform narrow-to-90deg generation and 90deg-to-180deg generation. The generated outputs are then fused with their aligned inputs to produce expanded equirectangular images for viewing. Our experimental results show that single-view-to-panoramic image generation using deep learning is both feasible and promising.