  Estimation of intrinsic images still remains a challenging task due to
weaknesses of ground-truth datasets, which either are too small or present
non-realistic issues. On the other hand, end-to-end deep learning architectures
start to achieve interesting results that we believe could be improved if
important physical hints were not ignored. In this work, we present a twofold
framework: (a) a flexible generation of images overcoming some classical
dataset problems such as larger size jointly with coherent lighting appearance;
and (b) a flexible architecture tying physical properties through intrinsic
losses. Our proposal is versatile, presents low computation time, and achieves
state-of-the-art results.
