SARGes: Semantically Aligned Reliable Gesture Generation via Intent Chain

SARGes: Semantically Aligned Reliable Gesture Generation via Intent Chain Nan Gao 1, Yihua Bao 2, Dongdong Weng 2, Jiayi Zhao 2, Jia Li 1, Y an Zhou 3, Pengfei Wan 3, and Di Zhang 3 Abstract -- Co-speech gesture generation enhances human-computer interaction realism through speech-synchronized gesture synthesis. However, generating semantically meaningful gestures remains a challenging problem. We propose SARGes, a novel framework that leverages large language models (LLMs) to parse speech content and generate reliable semantic gesture labels, which subsequently guide the synthesis of meaningful co-speech gestures. First, we constructed a comprehensive co-speech gesture ethogram and developed an LLM-based intent chain reasoning mechanism that systematically parses and decomposes gesture semantics into structured inference steps following ethogram criteria, effectively guiding LLMs to generate context-aware gesture labels. Subsequently, we constructed an intent chain-annotated text-to-gesture label dataset and trained a lightweight gesture label generation model, which then guides the generation of credible and semantically coherent co-speech gestures. Experimental results demonstrate that SARGes achieves highly semantically-aligned gesture labeling (50.2% accuracy) with efficient single-pass inference (0.4 seconds). The proposed method provides an interpretable intent reasoning pathway for semantic gesture synthesis. I. INTRODUCTION Gestures in human communication significantly enhance semantic transmission, emotional expression, and conversational flow [1].