Model-Based Deep Portrait Relighting

Like most computer vision problems the relighting of portrait face images is more and more being entirely formulated as a deep learning problem. However, data-driven approaches need a detailed and exhaustive database to work on and the creation of ground truth data is tedious and oftentimes technically complex. At the same time, networks get bigger and deeper. Knowledge about the problem statement, scene structure, and physical laws are often neglected. In this paper, we propose to encompass prior knowledge for relighting directly in the network learning process, adding model-based building blocks to the training. Thereby, we improve the learning speed and effectiveness of the network, thus performing better even with a restricted dataset. We demonstrate through an ablation study that the proposed model-based building blocks improve the network's training and enhance the generated images compared with the naive approach.