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.
翻译:手势合成已经成为一个重要的研究领域,致力于产生与语音或文本输入相对应的上下文适当和自然的手势。虽然基于深度学习的方法取得了显着进展,但它们经常忽视文本中存在的丰富语义信息,导致手势不够表达和有意义。我们提出了GesGPT,一种新颖的手势生成方法,利用大型语言模型(LLMs),如GPT的语义分析能力。通过充分利用LLMs进行文本分析的优势,我们设计了提示来从文本输入中提取与手势相关的信息。我们的方法涉及开发提示原则,将手势生成转化为基于GPT的意图分类问题,并利用精心策划的手势库和集成模块生成语义上丰富的共话手势。实验结果表明,GesGPT有效地生成上下文适当和富有表现力的手势,为语义共话手势生成提供了新的视角。