Descriptive and Generative Representations for Modeling and Interpreting Human Movement

Human movement analysis requires representations that allow for effective encoding and interpretation of spatiotemporally varying data. While several types of representation exist to encode elaborate movements (e.g. choreographies), they only bear a symbolic resemblance to what can be measured; trajectories of points on the body and joint angles. In this paper, we attempt to bridge the gap between the extremes of low-level encoding and high-level encoding. To facilitate this, we first introduce a representation based on dance notation. We then introduce a second notation based on L-systems. We show how the latter representation falls in the middle of the range of notations, allowing us to notate, encode, and synthesize various movements. We show the applicability of these representations by presenting animations created by an input of dance notation.