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.

This paper presents the CASIA-GO entry to the Generation and Evaluation of Non-verbal Behaviour for Embedded Agents (GENEA) Challenge 2023. The system is originally designed for few-shot scenarios such as generating gestures with the style of any in-the-wild target speaker from short speech samples. Given a group of reference speech data including gesture sequences, audio, and text, it first constructs a gesture motion graph that describes the soft gesture units and interframe continuity inside the speech, which is ready to be used for new rhythmic and semantic gesture reenactment by pathfinding when test audio and text are provided. We randomly choose one clip from the training data for one test clip to simulate a few-shot scenario and provide compatible results for subjective evaluations. Despite the 0.25% average utilization of the whole training set for each clip in the test set and the 17.5% total utilization of the training set for the whole test set, the system succeeds in providing valid results and ranks in the top 1/3 in the appropriateness for agent speech evaluation.