Omni-Directional Image Generation from Single Snapshot Image
This addresses the limited availability of ODIs for VR, robotics, and deep learning training, offering a novel task to expand content creation.
The paper tackles the problem of generating omni-directional images (ODI) from a single snapshot image, proposing a conditional generative adversarial network with class-conditioned convolution layers to enable easy creation of VR content using smartphone cameras.
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.