  Omnidirectional images (ODIs) have become increasingly popular, as their
large field-of-view (FoV) can offer viewers the chance to freely choose the
view directions in immersive environments such as virtual reality. The M\"obius
transformation is typically employed to further provide the opportunity for
movement and zoom on ODIs, but applying it to the image level often results in
blurry effect and aliasing problem. In this paper, we propose a novel deep
learning-based approach, called \textbf{OmniZoomer}, to incorporate the
M\"obius transformation into the network for movement and zoom on ODIs. By
learning various transformed feature maps under different conditions, the
network is enhanced to handle the increasing edge curvatures, which alleviates
the blurry effect. Moreover, to address the aliasing problem, we propose two
key components. Firstly, to compensate for the lack of pixels for describing
curves, we enhance the feature maps in the high-resolution (HR) space and
calculate the transformed index map with a spatial index generation module.
Secondly, considering that ODIs are inherently represented in the spherical
space, we propose a spherical resampling module that combines the index map and
HR feature maps to transform the feature maps for better spherical correlation.
The transformed feature maps are decoded to output a zoomed ODI. Experiments
show that our method can produce HR and high-quality ODIs with the flexibility
to move and zoom in to the object of interest. Project page is available at
http://vlislab22.github.io/OmniZoomer/.
