  The requirement for paired shadow and shadow-free images limits the size and
diversity of shadow removal datasets and hinders the possibility of training
large-scale, robust shadow removal algorithms. We propose a shadow removal
method that can be trained using only shadow and non-shadow patches cropped
from the shadow images themselves. Our method is trained via an adversarial
framework, following a physical model of shadow formation. Our central
contribution is a set of physics-based constraints that enables this
adversarial training. Our method achieves competitive shadow removal results
compared to state-of-the-art methods that are trained with fully paired shadow
and shadow-free images. The advantages of our training regime are even more
pronounced in shadow removal for videos. Our method can be fine-tuned on a
testing video with only the shadow masks generated by a pre-trained shadow
detector and outperforms state-of-the-art methods on this challenging test. We
illustrate the advantages of our method on our proposed video shadow removal
dataset.
