  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.
