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The generation of realistic and contextually relevant co-speech gestures is achallenging yet increasingly important task in the creation of multimodalartificial agents. Prior methods focused on learning a direct correspondencebetween co-speech gesture representations and produced motions, which createdseemingly natural but often unconvincing gestures during human assessment. Wepresent an approach to pre-train partial gesture sequences using a generativeadversarial network with a quantization pipeline. The resulting codebookvectors serve as both input and output in our framework, forming the basis forthe generation and reconstruction of gestures. By learning the mapping of alatent space representation as opposed to directly mapping it to a vectorrepresentation, this framework facilitates the generation of highly realisticand expressive gestures that closely replicate human movement and behavior,while simultaneously avoiding artifacts in the generation process. We evaluateour approach by comparing it with established methods for generating co-speechgestures as well as with existing datasets of human behavior. We also performan ablation study to assess our findings. The results show that our approachoutperforms the current state of the art by a clear margin and is partiallyindistinguishable from human gesturing. We make our data pipeline and thegeneration framework publicly available.

AQ-GT: a Temporally Aligned and Quantized GRU-Transformer for Co-Speech Gesture Synthesis