CCFM: An Architecture for Realtime Gesture Generation byClustering Gestures by Communicative Function and Motion


Gestures augment speech by performing a variety of communicative functions in humans and virtual agents, and are often related to speech by complex semantic, rhetorical, prosodic, and affective elements. In this paper we briefly present an architecture for humanlike gesturing in virtual agents that is designed to realize complex speech-to-gesture mappings by exploiting existing machine-learning based parsing tools and techniques to extract these functional elements from speech. We then deeply explore the rhetorical branch of this architecture, objectively assessing specifically whether existing rhetorical parsing techniques can classify gestures into classes with distinct movement properties. To do this, we take a corpus of spontaneously generated gestures and correlate their movement to co-speech utterances. We cluster gestures based on their rhetorical properties, and then by their movement.Our objective analysis suggests that some rhetorical structures are identifiable by our movement features while others require further exploration. We explore possibilities behind these findings and propose future experiments that may further reveal nuances of the richness of the mapping between speech and motion. This work builds towards a real-time gesture generator which performs gestures that effectively convey rich communicative functions.

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Multiple Metaphors in Metaphoric Gesturing


The use of metaphoric gestures by speakers has long been known to influence thought in the viewer. What is less clear is the extent to which the expression of multiple metaphors in a single gesture reliably affect viewer interpretation. Additionally, gestures which express only one metaphor are not sufficient to explain the broad array of metaphoric gestures and metaphoric scenes that human speakers naturally produce. In this paper we address three issues related to the implementation of metaphoric gestures in virtual humans. First, we break down naturally occurring examples of multiple-metaphor gestures, as well as metaphoric scenes created by gesture sequences. Then, we show the importance of capturing multiple metaphoric aspects of gesture with a behavioral experiment using crowdsourced judgements of videos of alterations of the naturally occurring gestures. Finally, we discuss the challenges for computationally modeling metaphoric gestures that are raised by our findings.

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