Abstract:
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. In order to disentangle this embedding information without supervision, we exploit the assumption that some augmented 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 data-sets such as Youtube 8M and CelebA. Our experiments show that our method can correctly estimate scene illumination and generate realistic re-lit examples, without any supervision or a prior shape model. Compared to supervised methods, our approach has similar performance and avoids common lighting artifacts.
