In this article, we investigate the perception of gender from the motion of virtual humans under different emotional conditions and explore the effect of emotional bias on gender perception (e.g., anger being attributed to males more than females). As motion types can present different levels of physiological cues, we also explore how two types of motion (walking and conversations) are affected by emotional bias. Walking typically displays more physiological cues about gender (e.g., hip sway) and therefore is expected to be less affected by emotional bias. To investigate these effects, we used a corpus of captured facial and body motions from four male and four female actors, performing basic emotions through conversation and walk. We expected that the appearance of the model would also influence gender perception; therefore, we displayed both male and female motions on two virtual models of different sex. Two experiments were then conducted to assess gender judgments from these motions. In both experiments, participants were asked to rate how male or female they considered the motions to be under different emotional states, then classified the emotions to determine how accurately they were portrayed by actors. Overall, both experiments showed that gender ratings were affected by the displayed emotion. However, we found that conversations were influenced by gender stereotypes to a greater extent than walking motions. This was particularly true for anger, which was perceived as male on both male and female motions, and sadness, which was perceived as less male when portrayed by male actors. We also found a slight effect of the model when observing gender on different types of virtual models. These results have implications for the design and animation of virtual humans.
Abstract. Most information visualisation methods are based on abstract visual representations without any concrete manifestation in the "real world". However, a variety of abstract datasets can indeed be related to, and hence enriched by, real-world aspects. In these cases an additional virtual representation of the 3D object can help to gain a better insight into the connection between abstract and real-world issues. We demonstrate this approach with two prototype systems that combine information visualisation with 3D models in multiple coordinated views. The first prototype involves the visualisation of in-car communication traces. The 3D model of the car serves as one view among several and provides the user with information about the car's activities. LibViz, our second prototype, is based on a full screen 3D representation of a library building. Measured data is visualised in overlaid, semi-transparent windows to allow the user interpretation of the data in its spatial context of the library's 3D model. Based on the two prototypes, we identify the benefits and drawbacks of the approach, investigate aspects of coordination between the 3D model and the abstract visualisations, and discuss principals for a general approach.
In this paper, we investigate the ability of humans to determine the gender of conversing characters, based on facial and body cues for emotion. We used a corpus of simultaneously captured facial and body motions from four male and four female actors. In our Gender Rating task, participants were asked to rate how male or female they considered the motions to be, under different emotional states. In our Emotion Recognition task, participants were asked to classify the emotions, in order to determine how accurately perceived those emotions were. We found that gender perception was affected by emotion, where certain emotions facilitated gender determination while others masked it. We also found that there was no correlation between how accurate an emotion was portrayed and how much gender information was present in that motion. Finally, we found that the model used to display the motion did not affect gender perception of motion but did alter emotion recognition.
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