Abstract
Gesture synthesis has gained significant attention as a critical research area, focusing on producing contextually appropriate and natural gestures corresponding to speech or textual input. Although deep learning-based approaches have achieved remarkable progress, they often overlook the rich semantic information present in the text, leading to less expressive and meaningful gestures. We propose GesGPT, a novel approach to gesture generation that leverages the semantic analysis capabilities of Large Language Models (LLMs), such as GPT. By capitalizing on the strengths of LLMs for text analysis, we design prompts to extract gesture-related information from textual input. Our method entails developing prompt principles that transform gesture generation into an intention classification problem based on GPT, and utilizing a curated gesture library and integration module to produce semantically rich co-speech gestures. Experimental results demonstrate that GesGPT effectively generates contextually appropriate and expressive gestures, offering a new perspective on semantic co-speech gesture generation.
Abstract (translated)
手势合成作为一个重要的研究领域,重点是如何产生与语音或文本输入对应的适当、自然手势。尽管基于深度学习的方法已经取得了显著进展,但它们往往忽略了文本中丰富的语义信息,导致表达力和有意义的手势减少。我们提出了GesGPT,一种手势生成的新型方法,利用大型语言模型(LLM)如GPT的语义分析能力。通过利用LRM在文本分析方面的优势,我们设计Prompts从文本输入中提取手势相关的信息。我们的方法和方法包括开发Prompt Principles,将手势生成转换为基于GPT的意图分类问题,并利用 curated gesture 库和集成模块生产语义丰富的合并口语手势。实验结果表明,GesGPT有效地生成了适当的、表达性的手势,提供了语义合并口语手势生成的新视角。
URL
https://arxiv.org/abs/2303.13013