Ali, Ghazanfar Lee, Myungho Hwang, Jae-In 2024-01-19T17:03:59Z 2024-01-19T17:03:59Z 2022-01-10 2020-07 1546-4261 https://pubs.kist.re.kr/handle/201004/118429 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. English WILEY Automatic text-to-gesture rule generation for embodied conversational agents Article 10.1002/cav.1944 1 COMPUTER ANIMATION AND VIRTUAL WORLDS, v.31, no.4-5 COMPUTER ANIMATION AND VIRTUAL WORLDS 31 4-5 scie scopus 000566926000001 2-s2.0-85090218433 Computer Science, Software Engineering Computer Science Article computer animation gesture generation rule-based mapping social agents virtual agents