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.

Human Behavior Analysis in Uncontrolled Environmentscan be categorized in two main challenges:1) Feature extraction and 2) Behavior analysisfrom a set of corporal language vocabulary. Inthis work, we present our achievements characterizingsome simple behaviors from visual data ondifferent real applications and discuss our plan forfuture work: low level vocabulary definition frombag-of-gesture units and high level modelling andinference of human behaviors.