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This paper proposes a novel approach to generating omni-directional imagesfrom a single snapshot picture. The previous method has relied on thegenerative adversarial networks based on convolutional neural networks (CNN).Although this method has successfully generated omni-directional images, CNNhas two drawbacks for this task. First, since a convolutional layer onlyprocesses a local area, it is difficult to propagate the information of aninput snapshot picture embedded in the center of the omni-directional image tothe edges of the image. Thus, the omni-directional images created by theCNN-based generator tend to have less diversity at the edges of the generatedimages, creating similar scene images. Second, the CNN-based model requireslarge video memory in graphics processing units due to the nature of the deepstructure in CNN since shallow-layer networks only receives signals from alimited range of the receptive field. To solve these problems, MLPMixer-basedmethod was proposed in this paper. The MLPMixer has been proposed as analternative to the self-attention in the transformer, which captures long-rangedependencies and contextual information. This enables to propagate informationefficiently in the omni-directional image generation task. As a result,competitive performance has been achieved with reduced memory consumption andcomputational cost, in addition to increasing diversity of the generatedomni-directional images.

Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixer