GesGPT：基于ChatGPT文本解析的语音手势合成
GesGPT: Speech Gesture Synthesis With Text Parsing from ChatGPT
AI总结：
提出GesGPT方法，利用ChatGPT的语义分析能力，通过文本解析将手势生成转化为意图分类问题，并构建手势词典解耦专业与基础手势，实验证明能生成上下文适当且富有表现力的手势。
摘要：
手势合成作为一个关键研究领域已受到广泛关注，旨在生成与语音或文本输入相对应的上下文适当且自然的手势。尽管基于深度学习的方法取得了显著进展，但它们常常忽略文本中蕴含的丰富语义信息，导致生成的手势表现力不足、意义有限。在本文中，我们提出GesGPT，一种新颖的手势生成方法，利用大型语言模型（如ChatGPT）的语义分析能力。通过发挥LLM在文本分析方面的优势，我们采用一种受控方法，通过文本解析脚本生成并整合专业手势和基础手势，从而产生多样且有意义的手势。首先，我们的方法涉及开发提示原则，利用ChatGPT将手势生成转化为意图分类问题。我们还对强调词和语义词进行进一步分析，以辅助手势生成。随后，我们构建了一个带有多种语义注释的专业手势词典，将手势合成解耦为专业手势和基础手势。最后，我们将专业手势与基础手势合并。实验结果表明，GesGPT能有效生成上下文适当且富有表现力的手势。
英文摘要：
Gesture synthesis has gained significant attention as a critical research field, aiming to produce 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. In this letter, we propose GesGPT, a novel approach to gesture generation that leverages the semantic analysis capabilities of large language models , such as ChatGPT. By capitalizing on the strengths of LLMs for text analysis, we adopt a controlled approach to generate and integrate professional gestures and base gestures through a text parsing script, resulting in diverse and meaningful gestures. Firstly, our approach involves the development of prompt principles that transform gesture generation into an intention classification problem using ChatGPT. We also conduct further analysis on emphasis words and semantic words to aid in gesture generation. Subsequently, we construct a specialized gesture lexicon with multiple semantic annotations, decoupling the synthesis of gestures into professional gestures and base gestures. Finally, we merge the professional gestures with base gestures. Experimental results demonstrate that GesGPT effectively generates contextually appropriate and expressive gestures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
