Context. Social interaction between player avatars is one of the fundamentals of online multiplayer games, where text- and voice chat is used as standard methods for communication. To express emotions, players have the option to use emotes, which are text-based commands to play animations of the player’s avatar. Objectives. This study investigates if real-time facial capture could be preferred and more realistic, compared to typing emote commands to play facial animations when expressing emotions to other players. Methods. By using two methods for social interaction between players, a user study with 24 participants was conducted in a private conference room. An experiment was created where each participant tested the two methods to perform facial expressions, one consisting of typing emote commands, the other by performing with the calibrated participant’s face in real-time, with a web camera. Each participant performed and ranked the realism of each facial expression, in a survey for each method. A final survey could then determine the method that each participant had greater performance with and the method that was preferred the most. Results. The results showed that there was a difference of realism between facial expressions in both methods, where happiness was the most realistic. Disgust and sadness were however poorly rated when expressing these with the face. There was no difference of realism between the methods and which method participants preferred the most. There was however a difference in each participant’s performance with the two methods, where typing emote commands had higher performance. Conclusions. The results show that there is no difference of realism and preference, in typing emote commands to play facial animations and performing facial expressions in real-time with the face. There was, however, a difference in performing each method, where performing facial expressions with the face was the lowest. This confirms that the real-time facial capture technology needs improvements, to fully recognize and track the facial features of the human face.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-14710 |
| Date | January 2017 |
| Creators | Pettersson, Erik |
| Publisher | Blekinge Tekniska Högskola, Institutionen för kreativa teknologier |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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