Omni-Directional Image Generation from Single Snapshot Image

TL;DR

This paper introduces a novel task of generating omni-directional images from a single snapshot using a conditional GAN, enabling easier creation of VR content from standard cameras.

Contribution

It proposes a new computer vision task and a GAN-based method to generate full-view 360-degree images from a single image, expanding VR content creation capabilities.

Findings

Successfully generated omni-directional images from single snapshots.

Demonstrated potential for VR content creation using smartphones.

Enhanced data availability for training deep learning models.

Abstract

An omni-directional image (ODI) is the image that has a field of view covering the entire sphere around the camera. The ODIs have begun to be used in a wide range of fields such as virtual reality (VR), robotics, and social network services. Although the contents using ODI have increased, the available images and videos are still limited, compared with widespread snapshot images. A large number of ODIs are desired not only for the VR contents, but also for training deep learning models for ODI. For these purposes, a novel computer vision task to generate ODI from a single snapshot image is proposed in this paper. To tackle this problem, the conditional generative adversarial network was applied in combination with class-conditioned convolution layers. With this novel task, VR images and videos will be easily created even with a smartphone camera.