  In this paper, we tackle the challenging task of Panoramic Image-to-Image
translation (Pano-I2I) for the first time. This task is difficult due to the
geometric distortion of panoramic images and the lack of a panoramic image
dataset with diverse conditions, like weather or time. To address these
challenges, we propose a panoramic distortion-aware I2I model that preserves
the structure of the panoramic images while consistently translating their
global style referenced from a pinhole image. To mitigate the distortion issue
in naive 360 panorama translation, we adopt spherical positional embedding to
our transformer encoders, introduce a distortion-free discriminator, and apply
sphere-based rotation for augmentation and its ensemble. We also design a
content encoder and a style encoder to be deformation-aware to deal with a
large domain gap between panoramas and pinhole images, enabling us to work on
diverse conditions of pinhole images. In addition, considering the large
discrepancy between panoramas and pinhole images, our framework decouples the
learning procedure of the panoramic reconstruction stage from the translation
stage. We show distinct improvements over existing I2I models in translating
the StreetLearn dataset in the daytime into diverse conditions. The code will
be publicly available online for our community.
