![]() In the planning phase, it uses a deep graph convolutional generative model to synthesize relation graphs. PlanIT represents the "plan" for a scene via a relation graph, encoding objects as nodes and spatial/semantic relationships between objects as edges. ![]() With this in mind, we present PlanIT, a layout-generation framework that divides the problem into two distinct planning and instantiation phases. ![]() Our insight is that the object-oriented paradigm excels at high-level planning of how a room should be laid out, while the space-oriented paradigm performs well at instantiating a layout by placing objects in precise spatial configurations. We observe that prior work on scene synthesis is divided into two camps: object-oriented approaches (which reason about the set of objects in a scene and their configurations) and space-oriented approaches (which reason about what objects occupy what regions of space). We present a new framework for interior scene synthesis that combines a high-level relation graph representation with spatial prior neural networks.
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