Emotion control of unstructured dance movements

Motion capture technology has enabled the acquisition of high quality human motions for animating digital characters with extremely high fidelity. However, despite all the advances in motion editing and synthesis, it remains an open problem to modify pre-captured motions that are highly expressive, such as contemporary dances, for stylization and emotionalization. In this work, we present a novel approach for stylizing such motions by using emotion coordinates defined by the Russell's Circumplex Model (RCM). We extract and analyze a large set of body and motion features, based on the Laban Movement Analysis (LMA), and choose the effective and consistent features for characterizing emotions of motions. These features provide a mechanism not only for deriving the emotion coordinates of a newly input motion, but also for stylizing the motion to express a different emotion without having to reference the training data. Such decoupling of the training data and new input motions eliminates the necessity of manual processing and motion registration. We implement the two-way mapping between the motion features and emotion coordinates through Radial Basis Function (RBF) regression and interpolation, which can stylize free-style highly dynamic dance movements at interactive rates. Our results and user studies demonstrate the effectiveness of the stylization framework with a variety of dance movements exhibiting a diverse set of emotions.