  In this work, we present Semantic Gesticulator, a novel framework designed to
synthesize realistic gestures accompanying speech with strong semantic
correspondence. Semantically meaningful gestures are crucial for effective
non-verbal communication, but such gestures often fall within the long tail of
the distribution of natural human motion. The sparsity of these movements makes
it challenging for deep learning-based systems, trained on moderately sized
datasets, to capture the relationship between the movements and the
corresponding speech semantics. To address this challenge, we develop a
generative retrieval framework based on a large language model. This framework
efficiently retrieves suitable semantic gesture candidates from a motion
library in response to the input speech. To construct this motion library, we
summarize a comprehensive list of commonly used semantic gestures based on
findings in linguistics, and we collect a high-quality motion dataset
encompassing both body and hand movements. We also design a novel GPT-based
model with strong generalization capabilities to audio, capable of generating
high-quality gestures that match the rhythm of speech. Furthermore, we propose
a semantic alignment mechanism to efficiently align the retrieved semantic
gestures with the GPT's output, ensuring the naturalness of the final
animation. Our system demonstrates robustness in generating gestures that are
rhythmically coherent and semantically explicit, as evidenced by a
comprehensive collection of examples. User studies confirm the quality and
human-likeness of our results, and show that our system outperforms
state-of-the-art systems in terms of semantic appropriateness by a clear
margin.
