Cube: A Roblox View of 3D Intelligence
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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Foundation AI Team
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Tinghui Zhou
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Yiheng Zhu
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Xiaoxia Sun
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Xiaoxia Sun
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Charles Shang
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Daiqing Li
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Kangle Deng
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Alexander Weiss
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Alexander Weiss
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Christian Reiser
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David Baszucki
Cube: A Roblox View of 3D Intelligence
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.
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.
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