@inproceedings{47c6bdecdbc04a5f8109a8da196ebe50,
title = "A Survey on Large Language Models for Motion Generation from Text",
abstract = "Over these last years, Large Language Models (LLMs) have evolved as an ubiquitous tool for different applications. One of those rapidly growing applications is motion generation, traditionally performed by a specific human design. This paper provides an overview of how motion is generated from text with the use of Large Language Models, targeted at 3D human representations and robots. We describe the different methodologies and techniques of the most used architectures, and the motion generation process in order to achieve the final motion. This survey aims to provide a comprehensive resource for researchers and developers seeking to leverage the power of LLMs to bridge the gap between text understanding and motion generation.",
keywords = "Human Motion, Large Language Model (LLM), Motion Generation, Robot Motion",
author = "Andres Obludzyner and Fabio Zaldivar and Ramos, \{Oscar E.\}",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 31st IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2024 ; Conference date: 06-11-2024 Through 08-11-2024",
year = "2024",
doi = "10.1109/INTERCON63140.2024.10833491",
language = "English",
series = "Proceedings of the 2024 IEEE 31st International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of the 2024 IEEE 31st International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2024",
}