  What makes an image appear realistic? In this work, we are answering this
question from a data-driven perspective by learning the perception of visual
realism directly from large amounts of data. In particular, we train a
Convolutional Neural Network (CNN) model that distinguishes natural photographs
from automatically generated composite images. The model learns to predict
visual realism of a scene in terms of color, lighting and texture
compatibility, without any human annotations pertaining to it. Our model
outperforms previous works that rely on hand-crafted heuristics, for the task
of classifying realistic vs. unrealistic photos. Furthermore, we apply our
learned model to compute optimal parameters of a compositing method, to
maximize the visual realism score predicted by our CNN model. We demonstrate
its advantage against existing methods via a human perception study.
