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.
@article{arxiv.2310.17838,
title = {Real-time Animation Generation and Control on Rigged Models via Large Language Models},
author = {Han Huang and Fernanda De La Torre and Cathy Mengying Fang and Andrzej Banburski-Fahey and Judith Amores and Jaron Lanier},
journal= {arXiv preprint arXiv:2310.17838},
year = {2024}
}
Comments
Accepted to NeurIPS Workshop on ML for Creativity and Design 2023