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.

本文从数据驱动的角度回答了图像看起来真实的因素，并利用卷积神经网络（CNN）模型从大量数据中学习了视觉真实感知。该模型能够预测场景的视觉真实感，而无需任何人为标记，并应用于计算一种组合方法的最佳参数，以最大化CNN模型预测的视觉真实感得分。此外，通过人类感知研究，我们展示了它对现有方法的优势。

学习感知合成图像真实感的判别模型