Abstract. This paper presents a study of the dynamic coupling between a user and a virtual character during body interaction. Coupling is directly linked with other dimensions, such as co-presence, engagement, and believability, and was measured in an experiment that allowed users to describe their subjective feelings about those dimensions of interest. The experiment was based on a theatrical game involving the imitation of slow upper-body movements and the proposal of new movements by the user and virtual agent. The agent's behaviour varied in autonomy: the agent could limit itself to imitating the user's movements only, initiate new movements, or combine both behaviours. After the game, each participant completed a questionnaire regarding their engagement in the interaction, their subjective feeling about the co-presence of the agent, etc. Based on four main dimensions of interest, we tested several hypotheses against our experimental results, which are discussed here.
Abstract-This paper presents a robust and anticipative realtime gesture recognition and its motion quality analysis module. By utilizing a motion capture device, the system recognizes gestures performed by a human, where the recognition process is based on skeleton analysis and motion features computation. Gestures are collected from a single person. Skeleton joints are used to compute features which are stored in a reference database, and Principal Component Analysis (PCA) is computed to select the most important features, useful in discriminating gestures. During real-time recognition, using distance measures, real-time selected features are compared to the reference database to find the most similar gesture. Our evaluation results show that: i) recognition delay is similar to human recognition delay, ii) our module can recognize several gestures performed by different people and is morphology-independent, and iii) recognition rate is high: all gestures are recognized during gesture stroke. Results also show performance limits.
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