LLMR: Real-time Prompting of Interactive Worlds using Large Language Models-Reference-Cited by-同舟云学术

LLMR: Real-time Prompting of Interactive Worlds using Large Language Models

Published:2024-05-11 Issue: Volume: Page:1-22
ISSN:
Container-title:Proceedings of the CHI Conference on Human Factors in Computing Systems
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Author:

De La Torre Fernanda¹^ORCID,Fang Cathy Mengying²^ORCID,Huang Han³^ORCID,Banburski-Fahey Andrzej⁴^ORCID,Amores Fernandez Judith⁵^ORCID,Lanier Jaron⁴^ORCID

Affiliation:

1. BCS, MIT, United States and Microsoft, United States

2. MIT Media Lab, United States and Microsoft, United States

3. Mathematics, Rensselaer Polytechnic Institute, United States and Microsoft, United States

4. Microsoft, United States

5. Microsoft Research, Microsoft, United States

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3613904.3642579

Reference64 articles.

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2. Sanghwan Bae, Donghyun Kwak, Soyoung Kang, Min Young Lee, Sungdong Kim, Yuin Jeong, Hyeri Kim, Sang-Woo Lee, Woomyoung Park, and Nako Sung. 2022. Keep Me Updated! Memory Management in Long-Term Conversations. ArXiv Preprint ArXiv:2210.08750 (2022). https://arxiv.org/pdf/2402.11975

3. Erik Bethke. 2003. Game Development and Production. Wordware Publishing, Inc.

4. HexPlane: A Fast Representation for Dynamic Scenes

5. Yifei Cheng Yukang Yan Xin Yi Yuanchun Shi and David Lindlbauer. 2021. Semanticadapt: Optimization-Based Adaptation of Mixed Reality Layouts Leveraging Virtual-Physical Semantic Connections. In The 34th Annual ACM Symposium on User Interface Software and Technology. 282–297. https://doi.org/10.1145/3472749.3474750