Modeling Social Interaction Based on Joint Motion Significance

Abstract

In this paper, we propose a method to model social interaction between a human and a virtual avatar. To this end, two human performers fist perform social interactions according to the Learning from Demonstration paradigm. Then, the relative relevance of all joints of both performers should be reasonably modeled based on human demonstrations. However, among all possible combinations of relative joints, it is necessary to select only some of the combinations that play key roles in social interaction. We select such significant features based on the joint motion significance, which is a metric to measure the significance degree by calculating both temporal entropy and spatial entropy of all human joints from a Gaussian mixture model. To evaluate our proposed method, we performed experiments on five social interactions: hand shaking, hand slapping, shoulder holding, object passing, and target kicking. In addition, we compared our method to existing modeling methods using different metrics, such as principal component analysis and information gain.