  We propose a self-supervised method for image relighting of single view
images in the wild. The method is based on an auto-encoder which deconstructs
an image into two separate encodings, relating to the scene illumination and
content, respectively. In order to disentangle this embedding information
without supervision, we exploit the assumption that some augmentation
operations do not affect the image content and only affect the direction of the
light. A novel loss function, called spherical harmonic loss, is introduced
that forces the illumination embedding to convert to a spherical harmonic
vector. We train our model on large-scale datasets such as Youtube 8M and
CelebA. Our experiments show that our method can correctly estimate scene
illumination and realistically re-light input images, without any supervision
or a prior shape model. Compared to supervised methods, our approach has
similar performance and avoids common lighting artifacts.
