Learning Object Context for Novel-view Scene Layout Generation

Novel-view prediction of a scene has many applications. Existing works mainly focus on generating novel-view images via pixel-wise prediction in the image space, often resulting in severe ghosting and blurry artifacts. In this paper, we make the first attempt to explore novel-view prediction in the layout space, and introduce the new problem of novel-view scene layout generation. Given a single scene layout and the camera transformation as inputs, our goal is to generate a plausible scene layout for a specified viewpoint. Such a problem is challenging as it involves accurate understanding of the 3D geometry and semantics of the scene from as little as a single 2D scene layout. To tackle this challenging problem, we propose a deep model to capture contextualized object representation by explicitly modeling the object context transformation in the scene. The contextualized object representation is essential in generating geometrically and semantically consistent scene layouts of different views. Experiments show that our model outperforms several strong baselines on many indoor and outdoor scenes, both qualitatively and quantitatively. We also show that our model enables a wide range of applications, including novel-view image synthesis, novel-view image editing, and amodal object estimation.