Action-GPT: Leveraging Large-scale Language Models for Improved and Generalized Zero Shot Action Generation

• URL: http://arxiv.org/abs/2211.15603v2
• Date: Wed, 30 Nov 2022 13:13:29 GMT
• Title: Action-GPT: Leveraging Large-scale Language Models for Improved and Generalized Zero Shot Action Generation
• Authors: Sai Shashank Kalakonda, Shubh Maheshwari, Ravi Kiran Sarvadevabhatla
• Abstract summary: Action-GPT is a framework for incorporating Large Language Models into text-based action generation models. We show that utilizing detailed descriptions instead of the original action phrases leads to better alignment of text and motion spaces.
• Score: 8.753131760384964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Action-GPT, a plug and play framework for incorporating Large Language Models (LLMs) into text-based action generation models. Action phrases in current motion capture datasets contain minimal and to-the-point information. By carefully crafting prompts for LLMs, we generate richer and fine-grained descriptions of the action. We show that utilizing these detailed descriptions instead of the original action phrases leads to better alignment of text and motion spaces. Our experiments show qualitative and quantitative improvement in the quality of synthesized motions produced by recent text-to-motion models. Code, pretrained models and sample videos will be made available at https://actiongpt.github.io

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