Reasoning Gestures: LLM-Inferred Communicative Functions for Co-Speech Gesture Generation

When humans speak, gestures often realize communicative functions (\textit{e.g.}, emphasizing key content, indicating referents, or depicting concepts). Most co-speech gesture generation methods, however, condition primarily on low-level cues (speech audio or transcripts) and do not explicitly represent these higher-level functions, leading to motions that synchronize with speech but remain under-specified in communicative meaning. To address this gap, we introduce \textbf{Reasoning Gestures}, a reasoning-grounded framework that conditions generation on interpretable communicative-function descriptions. We curate \textbf{InG: Interpreted Gestures} by augmenting BEAT-2 with sentence-level \emph{VLM-inferred gesture-function annotations} that describe the discourse role of each gesture segment in context. These labels are \emph{observer-centric interpretations} produced via constrained VLM reasoning rather than claims about speakers’ private mental states. We further propose a \textbf{Reasoning Gesture Motion Tokenizer} that injects function signals into tokenized motion representations, enabling function-aware synthesis that is temporally aligned and communicatively grounded. Experiments on BEAT-2 show consistent gains in semantic consistency and overall quality, establishing new state-of-the-art.