  Recently deep generative models have achieved impressive progress in modeling
the distribution of training data. In this work, we present for the first time
a generative model for 4D light field patches using variational autoencoders to
capture the data distribution of light field patches. We develop a generative
model conditioned on the central view of the light field and incorporate this
as a prior in an energy minimization framework to address diverse light field
reconstruction tasks. While pure learning-based approaches do achieve excellent
results on each instance of such a problem, their applicability is limited to
the specific observation model they have been trained on. On the contrary, our
trained light field generative model can be incorporated as a prior into any
model-based optimization approach and therefore extend to diverse
reconstruction tasks including light field view synthesis, spatial-angular
super resolution and reconstruction from coded projections. Our proposed method
demonstrates good reconstruction, with performance approaching end-to-end
trained networks, while outperforming traditional model-based approaches on
both synthetic and real scenes. Furthermore, we show that our approach enables
reliable light field recovery despite distortions in the input.
