{"id":990816,"date":"2023-12-08T11:39:17","date_gmt":"2023-12-08T19:39:17","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=990816"},"modified":"2024-05-01T15:08:51","modified_gmt":"2024-05-01T22:08:51","slug":"llmr-real-time-prompting-of-interactive-worlds-using-large-language-models","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/llmr-real-time-prompting-of-interactive-worlds-using-large-language-models\/","title":{"rendered":"Real-time Animation Generation and Control on Rigged Models via Large Language Models"},"content":{"rendered":"<p>We introduce a novel method for real-time animation control and generation on rigged models using natural language input. First, we embed a large language model (LLM) in Unity to output structured texts that can be parsed into diverse and realistic animations. Second, we illustrate LLM&#8217;s potential to enable flexible state transition between existing animations. We showcase the robustness of our approach through qualitative results on various rigged models and motions.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We introduce a novel method for real-time animation control and generation on rigged models using natural language input. First, we embed a large language model (LLM) in Unity to output structured texts that can be parsed into diverse and realistic animations. Second, we illustrate LLM&#8217;s potential to enable flexible state transition between existing animations. 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