Motion data retrieval based on statistic correlation between motion symbol space and language

Abstract

Captured human motion data are used so that humanoid robots or computer graphics (CG) characters can behave naturally. Because the motion capture system is expensive, and time-consuming process is needed to acquire motion data, technology that enables to reuse existing motion data efficiently is required. This paper proposes motion retrieval method with natural language word based on stochastic correlation between motion and language. We construct a space which has maximum correlation with motion pattern features and word features, and we use this space as search space for motion retrieval. Proto-symbol space, which represents the relationship of each symbolized motion patterns, is used as motion feature space. And as word feature, binary features are used which represent whether a word label is attached or not. Because the constructed search space has correlation between motion patterns and words, associative motion retrieval considering similarity of motion pattern or closeness of word meaning becomes possible. We validate proposed motion retrieval method by constructing motion database with captured human motion data.