title: Cube: A Roblox View of 3D Intelligence
summary: Foundation models trained on vast amounts of data have demonstrated
  remarkable reasoning and generation capabilities in the domains of text,
  images, audio and video. Our goal at Roblox is to build such a foundation model
  for 3D intelligence, a model that can support developers in producing all
  aspects of a Roblox experience, from generating 3D objects and scenes to
  rigging characters for animation to producing programmatic scripts describing
  object behaviors. We discuss three key design requirements for such a 3D
  foundation model and then present our first step towards building such a model.
  We expect that 3D geometric shapes will be a core data type and describe our
  solution for 3D shape tokenizer. We show how our tokenization scheme can be
  used in applications for text-to-shape generation, shape-to-text generation and
  text-to-scene generation. We demonstrate how these applications can collaborate
  with existing large language models (LLMs) to perform scene analysis and
  reasoning. We conclude with a discussion outlining our path to building a fully
  unified foundation model for 3D intelligence.
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paper_title: Cube: A Roblox View of 3D Intelligence
paper_summary: Foundation models trained on vast amounts of data have demonstrated
  remarkable reasoning and generation capabilities in the domains of text,
  images, audio and video. Our goal at Roblox is to build such a foundation model
  for 3D intelligence, a model that can support developers in producing all
  aspects of a Roblox experience, from generating 3D objects and scenes to
  rigging characters for animation to producing programmatic scripts describing
  object behaviors. We discuss three key design requirements for such a 3D
  foundation model and then present our first step towards building such a model.
  We expect that 3D geometric shapes will be a core data type and describe our
  solution for 3D shape tokenizer. We show how our tokenization scheme can be
  used in applications for text-to-shape generation, shape-to-text generation and
  text-to-scene generation. We demonstrate how these applications can collaborate
  with existing large language models (LLMs) to perform scene analysis and
  reasoning. We conclude with a discussion outlining our path to building a fully
  unified foundation model for 3D intelligence.
paper_ai_summary: A foundation model for 3D intelligence tokenizes 3D shapes and integrates with LLMs for tasks like text-to-shape, shape-to-text, and text-to-scene generation, enabling scene analysis and reasoning in 3D development.
paper_ai_keywords: 3D foundation model text-to-shape generation shape-to-text generation text-to-scene generation large language models (LLMs) 3D shape tokenizer scene analysis reasoning