Human Animation Using Nonparametric Regression

This article describes how to build a nonparametric regression model for the prediction of human motions using motion capture data. The method works by selecting a few similar motions from a database and then averaging them. An appropriate parameterization is developed for representing the motions composed of the trajectories of the endpoints such as the hands and stretch pivot coordinates to represent the interior joints such as the shoulders or knees. Within this representation, local averaging may sensibly occur while preserving specified constraints like the length of the body segments and final location of the endpoints. A nearest neighbor regression like method is developed and a cross-validation procedure implemented for selecting the best number of neighbors and appropriate notions of similarity in the motions selected for averaging. The methodology is illustrated with the development of a prediction model for two-handed standing lifts with applications to ergonomics.