Modeling the Interpretation of Animations to Help Improve Emotional Expression

More and more synthetic characters are being used in applications worldwide. When designing synthetic characters that interact with human users, the adequate expression of emotions is critical to achieving more believable and effective communication. Yet multiple works show that the correct recognition of emotion in synthetic characters is often hard to achieve and harder to understand. To better understand how emotions are recognized, we propose the Triad Affect Interpretation (TAI) method that creates a model of how users perceive the animations of emotions of specific synthetic characters, with the additional intent of helping the development of future animations and improving the way emotions are communicated so that they are more easily recognized. The method uses two questionnaires that focus on a set of animations of emotions taken from the synthetic characters under development. The purpose of the first questionnaire is to elicit meaningful constructs from the participants through content analysis. The second questionnaire asks participants to rate the same animations against the selected constructs. By using principal component analysis and cluster analysis, we then create a model of the relevant factors to the perception of emotions that can inform future improvements of the animations related to the expression of emotions.