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Indoor scene synthesis involves automatically picking and placing furnitureappropriately on a floor plan, so that the scene looks realistic and isfunctionally plausible. Such scenes can serve as homes for immersive 3Dexperiences, or be used to train embodied agents. Existing methods for thistask rely on labeled categories of furniture, e.g. bed, chair or table, togenerate contextually relevant combinations of furniture. Whether heuristic orlearned, these methods ignore instance-level visual attributes of objects, andas a result may produce visually less coherent scenes. In this paper, weintroduce an auto-regressive scene model which can output instance-levelpredictions, using general purpose image embedding based on CLIP. This allowsus to learn visual correspondences such as matching color and style, andproduce more functionally plausible and aesthetically pleasing scenes.Evaluated on the 3D-FRONT dataset, our model achieves SOTA results in scenesynthesis and improves auto-completion metrics by over 50%. Moreover, ourembedding-based approach enables zero-shot text-guided scene synthesis andediting, which easily generalizes to furniture not seen during training.

CLIP-Layout: Style-Consistent Indoor Scene Synthesis with Semantic Furniture Embedding