Real-time Animation Generation and Control on Rigged Models via Large Language Models
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909107763544064 |
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| author | Huang, Han De La Torre, Fernanda Fang, Cathy Mengying Banburski-Fahey, Andrzej Amores, Judith Lanier, Jaron |
| author_facet | Huang, Han De La Torre, Fernanda Fang, Cathy Mengying Banburski-Fahey, Andrzej Amores, Judith Lanier, Jaron |
| contents | 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'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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_17838 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Real-time Animation Generation and Control on Rigged Models via Large Language Models Huang, Han De La Torre, Fernanda Fang, Cathy Mengying Banburski-Fahey, Andrzej Amores, Judith Lanier, Jaron Graphics Artificial Intelligence 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'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. |
| title | Real-time Animation Generation and Control on Rigged Models via Large Language Models |
| topic | Graphics Artificial Intelligence |
| url | https://arxiv.org/abs/2310.17838 |