Manipulating Attributes of Natural Scenes via Hallucination

Citations Over TimeTop 22% of 2019 papers

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 that can hallucinate images of a scene as if they were taken in a different season (e.g., during winter), weather condition (e.g., on a cloudy day), or at a different 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 demonstrates the effectiveness of our approach against the competing methods.

Related Papers

• → Tell Me Where I Am: Object-Level Scene Context Prediction(2019)12 cited
• → User guided 3D scene enrichment(2016)9 cited
• → SceneSeer: 3D Scene Design with Natural Language(2017)19 cited
• Recognizing objects and reasoning about their interactions(2010)