Paper Info
Title
Automatic Text-To-Gesture Rule Generation For Embodied Conversational Agents
Abstract
Interactions with embodied conversational agents can be enhanced using human-like co-speech gestures. Traditionally, rule-based co-speech gesture mapping has been utilized for this purpose. However, the creation of this mapping is laborious and often requires human experts. Moreover, human-created mapping tends to be limited, therefore prone to generate repeated gestures. In this article, we present an approach to automate the generation of rule-based co-speech gesture mapping from publicly available large video data set without the intervention of human experts. At run-time, word embedding is utilized for rule searching to get the semantic-aware, meaningful, and accurate rule. The evaluation indicated that our method achieved comparable performance with the manual map generated by human experts, with a more variety of gestures activated. Moreover, synergy effects were observed in users' perception of generated co-speech gestures when combined with the manual map.
Year
DOI
Venue
2020
10.1002/cav.1944
COMPUTER ANIMATION AND VIRTUAL WORLDS
Keywords
DocType
Volume
computer animation, gesture generation, rule-based mapping, social agents, virtual agents
Journal
31
Issue
ISSN
Citations 
4-5
1546-4261
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Ghazanfar Ali101.35
Myungho Lee213824.39
Jae-In Hwang3268.28