SHOW-1 Model

In this work we present our approach to generating high-quality episodic content for IP’s (Intellectual Property) using large language models (LLMs), custom state-of-the art diffusion models and our multi-agent simulation for contextualization, story progression and behavioral control.

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Creative limitations of existing generative AI Systems

 

Current generative AI systems such as Stable Diffusion (Image Generator) and ChatGPT (Large Language Model) excel at short-term general tasks through prompt engineering. However, they do not provide contextual guidance or intentionality to either a user or an automated generative story system (showrunner1) as part of a long-term creative process which is often essential to producing high-quality creative works, especially in the context of existing IP’s.

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Living with uncertainty

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By using a multi-agent2 simulation as part of the process we can make use of data points such as a character’s history, their goals and emotions, simulation events and localities to generate scenes and image assets more coherently and consistently aligned with the IP story world. The IP-based simulation also provides a clear, well known context to the user which allows them to judge the generated story more easily. Moreover, by allowing them to exert behavioral control over agents, observe their actions and engage in interactive conversations, the user’s expectations and intentions are formed which we then funnel into a simple prompt to kick off the generation process... READ MORE

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