  Deep image relighting is gaining more interest lately, as it allows photo
enhancement through illumination-specific retouching without human effort.
Aside from aesthetic enhancement and photo montage, image relighting is
valuable for domain adaptation, whether to augment datasets for training or to
normalize input test data. Accurate relighting is, however, very challenging
for various reasons, such as the difficulty in removing and recasting shadows
and the modeling of different surfaces. We present a novel dataset, the Virtual
Image Dataset for Illumination Transfer (VIDIT), in an effort to create a
reference evaluation benchmark and to push forward the development of
illumination manipulation methods. Virtual datasets are not only an important
step towards achieving real-image performance but have also proven capable of
improving training even when real datasets are possible to acquire and
available. VIDIT contains 300 virtual scenes used for training, where every
scene is captured 40 times in total: from 8 equally-spaced azimuthal angles,
each lit with 5 different illuminants.
