Generating Scenes with Latent Object Models

We introduce a structured latent variable model that learns the underlying data-generating process for a dataset of scenes. Our goals are to obtain a compositional scene representation and to perform scene generation by modeling statistical relationships between scenes as well as between objects within a scene. To make inference tractable, we take inspiration from visual topic models and introduce an interpretable hierarchy of scene-level and object-level latent variables (i.e., slots). Since generating scenes requires modeling dependencies between objects, we cannot make a bag-of-words assumption to simplify inference. Moreover, assuming that slots are generated with an autoregressive prior requires decomposing scenes sequentially during inference which has known limitations. Our approach is to assume that the assignment of objects to slots during generation is a deterministic function of the scene latent variable. This removes the need for sequential scene decomposition and enables us to propose an inference algorithm that uses orderless scene decomposition to indirectly estimate an ordered slot posterior. Qualitative and quantitative analysis establishes that our approach successfully learns a smoothly traversable scene-level latent space. The hierarchy of scene and slot variables improves the ability of slot-based models to generate samples displaying complex object relations. We also demonstrate that the learned hierarchy of representations can be used for a scene-retrieval application with object-centric re-ranking.