  Photographers routinely compose multiple manipulated photos of the same scene
(layers) into a single image, which is better than any individual photo could
be alone. Similarly, 3D artists set up rendering systems to produce layered
images to contain only individual aspects of the light transport, which are
composed into the final result in post-production. Regrettably, both approaches
either take considerable time to capture, or remain limited to synthetic
scenes. In this paper, we suggest a system to allow decomposing a single image
into a plausible shading decomposition (PSD) that approximates effects such as
shadow, diffuse illumination, albedo, and specular shading. This decomposition
can then be manipulated in any off-the-shelf image manipulation software and
recomposited back. We perform such a decomposition by learning a convolutional
neural network trained using synthetic data. We demonstrate the effectiveness
of our decomposition on synthetic (i.e., rendered) and real data (i.e.,
photographs), and use them for common photo manipulation, which are nearly
impossible to perform otherwise from single images.
