Modeling Spatial and Temporal Variation in Motion Data

摘要

We present a novel method to model and synthesize variation in motion data. Given a few examples of a particular type of motion as input; we learn a generative model that is able to synthesize a family of spatial and temporal variants that are statistically similar to the input examples. The new variants retain the features of the original examples; but are not exact copies of them. We learn a Dynamic Bayesian Network model from the input examples that enables us to capture properties of conditional independence in the data; and model it using a multivariate probability distribution. We present results for a variety of human motion; and 2D handwritten characters. We perform a user study to show that our new variants are less repetitive than typical game and crowd simulation approaches of re-playing a small number of existing motion clips. Our technique can synthesize new variants efficiently and has a small memory requirement.