Name
Abstract | ||
|---|---|---|
Iconic gestures are used to depict physical objects mentioned in speech, and the gesture form is assumed to be based on the image of a given object in the speaker’s mind. Using this idea, this study proposes a model that learns iconic gesture forms from an image representation obtained from pictures of physical entities. First, we collect a set of pictures of each entity from the web, and create an average image representation from them. Subsequently, the average image representation is fed to a fully connected neural network to decide the gesture form. In the model evaluation experiment, our two-step gesture form selection method can classify seven types of gesture forms with over 62% accuracy. Furthermore, we demonstrate an example of gesture generation in a virtual agent system in which our model is used to create a gesture dictionary that assigns a gesture form for each entry word in the dictionary.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3340555.3353736 | ICMI |
Keywords | Field | DocType |
Gesture generation, Iconic gesture, Image representation, Deep neural network | Computer vision,Gesture,Computer science,Image representation,Human–computer interaction,Artificial intelligence | Conference |
ISBN | Citations | PageRank |
978-1-4503-6860-5 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fumio Nihei | 1 | 18 | 4.52 |
| Yukiko Nakano | 2 | 501 | 62.37 |
| Ryuichiro Higashinaka | 3 | 341 | 47.27 |
| Ryo Ishii | 4 | 155 | 16.59 |