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3D simulated environments play a critical role in Embodied AI, but theircreation requires expertise and extensive manual effort, restricting theirdiversity and scope. To mitigate this limitation, we present Holodeck, a systemthat generates 3D environments to match a user-supplied prompt fullyautomatedly. Holodeck can generate diverse scenes, e.g., arcades, spas, andmuseums, adjust the designs for styles, and can capture the semantics ofcomplex queries such as "apartment for a researcher with a cat" and "office ofa professor who is a fan of Star Wars". Holodeck leverages a large languagemodel (GPT-4) for common sense knowledge about what the scene might look likeand uses a large collection of 3D assets from Objaverse to populate the scenewith diverse objects. To address the challenge of positioning objectscorrectly, we prompt GPT-4 to generate spatial relational constraints betweenobjects and then optimize the layout to satisfy those constraints. Ourlarge-scale human evaluation shows that annotators prefer Holodeck overmanually designed procedural baselines in residential scenes and that Holodeckcan produce high-quality outputs for diverse scene types. We also demonstratean exciting application of Holodeck in Embodied AI, training agents to navigatein novel scenes like music rooms and daycares without human-constructed data,which is a significant step forward in developing general-purpose embodiedagents.

Holodeck: Language Guided Generation of 3D Embodied AI Environments