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Due to their significance in human communication, the automatic generation ofco-speech gestures in artificial embodied agents has received a lot ofattention. Although modern deep learning approaches can generaterealistic-looking conversational gestures from spoken language, they often lackthe ability to convey meaningful information and generate contextuallyappropriate gestures. This paper presents an augmented approach to thegeneration of co-speech gestures that additionally takes into account givenform and meaning features for the gestures. Our framework effectively acquiresthis information from a small corpus with rich semantic annotations and alarger corpus without such information. We provide an analysis of the effectsof distinctive feature targets and we report on a human rater evaluation studydemonstrating that our framework achieves semantic coherence and personperception on the same level as human ground truth behavior. We make our datapipeline and the generation framework publicly available.

Augmented Co-Speech Gesture Generation: Including Form and Meaning Features to Guide Learning-Based Gesture Synthesis