Unbounded: A Generative Infinite Game of Character Life Simulation
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929568382713856 |
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| author | Li, Jialu Li, Yuanzhen Wadhwa, Neal Pritch, Yael Jacobs, David E. Rubinstein, Michael Bansal, Mohit Ruiz, Nataniel |
| author_facet | Li, Jialu Li, Yuanzhen Wadhwa, Neal Pritch, Yael Jacobs, David E. Rubinstein, Michael Bansal, Mohit Ruiz, Nataniel |
| contents | We introduce the concept of a generative infinite game, a video game that transcends the traditional boundaries of finite, hard-coded systems by using generative models. Inspired by James P. Carse's distinction between finite and infinite games, we leverage recent advances in generative AI to create Unbounded: a game of character life simulation that is fully encapsulated in generative models. Specifically, Unbounded draws inspiration from sandbox life simulations and allows you to interact with your autonomous virtual character in a virtual world by feeding, playing with and guiding it - with open-ended mechanics generated by an LLM, some of which can be emergent. In order to develop Unbounded, we propose technical innovations in both the LLM and visual generation domains. Specifically, we present: (1) a specialized, distilled large language model (LLM) that dynamically generates game mechanics, narratives, and character interactions in real-time, and (2) a new dynamic regional image prompt Adapter (IP-Adapter) for vision models that ensures consistent yet flexible visual generation of a character across multiple environments. We evaluate our system through both qualitative and quantitative analysis, showing significant improvements in character life simulation, user instruction following, narrative coherence, and visual consistency for both characters and the environments compared to traditional related approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_18975 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unbounded: A Generative Infinite Game of Character Life Simulation Li, Jialu Li, Yuanzhen Wadhwa, Neal Pritch, Yael Jacobs, David E. Rubinstein, Michael Bansal, Mohit Ruiz, Nataniel Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Graphics Machine Learning We introduce the concept of a generative infinite game, a video game that transcends the traditional boundaries of finite, hard-coded systems by using generative models. Inspired by James P. Carse's distinction between finite and infinite games, we leverage recent advances in generative AI to create Unbounded: a game of character life simulation that is fully encapsulated in generative models. Specifically, Unbounded draws inspiration from sandbox life simulations and allows you to interact with your autonomous virtual character in a virtual world by feeding, playing with and guiding it - with open-ended mechanics generated by an LLM, some of which can be emergent. In order to develop Unbounded, we propose technical innovations in both the LLM and visual generation domains. Specifically, we present: (1) a specialized, distilled large language model (LLM) that dynamically generates game mechanics, narratives, and character interactions in real-time, and (2) a new dynamic regional image prompt Adapter (IP-Adapter) for vision models that ensures consistent yet flexible visual generation of a character across multiple environments. We evaluate our system through both qualitative and quantitative analysis, showing significant improvements in character life simulation, user instruction following, narrative coherence, and visual consistency for both characters and the environments compared to traditional related approaches. |
| title | Unbounded: A Generative Infinite Game of Character Life Simulation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language Graphics Machine Learning |
| url | https://arxiv.org/abs/2410.18975 |