Part of the contribution of this paper is about preventing that specific problem. “We also seek to eliminate artifacts that can be seen in the results of prior

Part of the contribution of this paper is about preventing that specific problem. “We also seek to eliminate artifacts that can be seen in the results of prior deep-learning approaches, which often hallucinate objects. To this end, we analyze the datasets that are commonly used for photorealism enhancement. Our analysis reveals that their scene layouts differ in ways that can explain artifacts commonly seen in prior work. To better align the datasets and alleviate the artifacts, we propose a new strategy for sampling image patches during training. We further design a new adversarial training objective that facilitates enhancements that are geometrically and semantically consistent with the content of the input image.”