  Single-view intrinsic image decomposition is a highly ill-posed problem, and
so a promising approach is to learn from large amounts of data. However, it is
difficult to collect ground truth training data at scale for intrinsic images.
In this paper, we explore a different approach to learning intrinsic images:
observing image sequences over time depicting the same scene under changing
illumination, and learning single-view decompositions that are consistent with
these changes. This approach allows us to learn without ground truth
decompositions, and to instead exploit information available from multiple
images when training. Our trained model can then be applied at test time to
single views. We describe a new learning framework based on this idea,
including new loss functions that can be efficiently evaluated over entire
sequences. While prior learning-based methods achieve good performance on
specific benchmarks, we show that our approach generalizes well to several
diverse datasets, including MIT intrinsic images, Intrinsic Images in the Wild
and Shading Annotations in the Wild.
