Abstract
In this study, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene. The key to our approach is a deep generative network which can hallucinate images of a scene as if they were taken at a different season (e.g. during winter), weather condition (e.g. in a cloudy day) or time of the day (e.g. at sunset). Once the scene is hallucinated with the given attributes, the corresponding look is then transferred to the input image while preserving the semantic details intact, giving a photo-realistic manipulation result. As the proposed framework hallucinates what the scene will look like, it does not require any reference style image as commonly utilized in most of the appearance or style transfer approaches. Moreover, it allows to simultaneously manipulate a given scene according to a diverse set of transient attributes within a single model, eliminating the need of training multiple networks per each translation task. Our comprehensive set of qualitative and quantitative results demonstrate the effectiveness of our approach against the competing methods.
Abstract (translated)
在本研究中，我们探索构建一个两阶段框架，使用户能够直接操作自然场景的高级属性。我们的方法的关键是一个深层的生成网络，它可以幻觉一个场景的图像，好像它们是在不同的季节（例如在冬季），天气条件（例如在阴天）或一天中的时间（例如在日落时）拍摄的）。一旦场景被给定属性产生幻觉，则相应的外观然后被转移到输入图像，同时保持语义细节完整，给出照片逼真的操纵结果。由于所提出的框架使场景看起来像幻觉，它不需要在大多数外观或样式转移方法中常用的任何参考样式图像。此外，它允许根据单个模型内的各种瞬态属性同时操纵给定场景，从而消除了每个翻译任务训练多个网络的需要。我们全面的定性和定量结果证明了我们的方法对竞争方法的有效性。
URL
https://arxiv.org/abs/1808.07413
