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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.15475 |
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| _version_ | 1866916850049220608 |
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| author | Foundation AI Team Bhat, Kiran Khanna, Nishchaie Channa, Karun Zhou, Tinghui Zhu, Yiheng Sun, Xiaoxia Shang, Charles Sudarshan, Anirudh Chu, Maurice Li, Daiqing Deng, Kangle Fauconnier, Jean-Philippe Verhulsdonck, Tijmen Agrawala, Maneesh Fatahalian, Kayvon Weiss, Alexander Reiser, Christian Chirravuri, Ravi Kiran Kandur, Ravali Pelaez, Alejandro Garg, Akash Palleschi, Michael Wang, Jessica Litz, Skylar Liu, Leon Li, Anying Harmon, David Liu, Derek Feng, Liangjun Goupil, Denis Kuczynski, Lukas Yoon, Jihyun Marri, Naveen Zhuang, Peiye Zhang, Yinan Yin, Brian Jiang, Haomiao van Workum, Marcel Lane, Thomas Erickson, Bryce Pathare, Salil Price, Kyle Han, Steve Wang, Yiqing Singh, Anupam Baszucki, David |
| author_facet | Foundation AI Team Bhat, Kiran Khanna, Nishchaie Channa, Karun Zhou, Tinghui Zhu, Yiheng Sun, Xiaoxia Shang, Charles Sudarshan, Anirudh Chu, Maurice Li, Daiqing Deng, Kangle Fauconnier, Jean-Philippe Verhulsdonck, Tijmen Agrawala, Maneesh Fatahalian, Kayvon Weiss, Alexander Reiser, Christian Chirravuri, Ravi Kiran Kandur, Ravali Pelaez, Alejandro Garg, Akash Palleschi, Michael Wang, Jessica Litz, Skylar Liu, Leon Li, Anying Harmon, David Liu, Derek Feng, Liangjun Goupil, Denis Kuczynski, Lukas Yoon, Jihyun Marri, Naveen Zhuang, Peiye Zhang, Yinan Yin, Brian Jiang, Haomiao van Workum, Marcel Lane, Thomas Erickson, Bryce Pathare, Salil Price, Kyle Han, Steve Wang, Yiqing Singh, Anupam Baszucki, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_15475 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cube: A Roblox View of 3D Intelligence Foundation AI Team Bhat, Kiran Khanna, Nishchaie Channa, Karun Zhou, Tinghui Zhu, Yiheng Sun, Xiaoxia Shang, Charles Sudarshan, Anirudh Chu, Maurice Li, Daiqing Deng, Kangle Fauconnier, Jean-Philippe Verhulsdonck, Tijmen Agrawala, Maneesh Fatahalian, Kayvon Weiss, Alexander Reiser, Christian Chirravuri, Ravi Kiran Kandur, Ravali Pelaez, Alejandro Garg, Akash Palleschi, Michael Wang, Jessica Litz, Skylar Liu, Leon Li, Anying Harmon, David Liu, Derek Feng, Liangjun Goupil, Denis Kuczynski, Lukas Yoon, Jihyun Marri, Naveen Zhuang, Peiye Zhang, Yinan Yin, Brian Jiang, Haomiao van Workum, Marcel Lane, Thomas Erickson, Bryce Pathare, Salil Price, Kyle Han, Steve Wang, Yiqing Singh, Anupam Baszucki, David Computer Vision and Pattern Recognition 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. |
| title | Cube: A Roblox View of 3D Intelligence |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.15475 |