A Generative Model for Motion Synthesis and Blending Using Probability Density Estimation

The main focus of this paper is to present a method of reusing motion captured data by learning a generative model of motion. The model allows synthesis and blending of cyclic motion, whilst providing it with the style and realism present in the original data. This is achieved by projecting the data into a lower dimensional space and learning a multivariate probability distribution of the motion sequences. Functioning as a generative model, the probability density estimation is used to produce novel motions from the model and gradient based optimisation used to generate the final animation. Results show plausible motion generation and lifelike blends between different actions.

• Type: Conference
• Year: 2008
• Where: AMDO
• Authors: Dumebi Okwechime, Richard Bowden
• Keywords: AMDO 2008, Computer Graphics, Generative Model, Model Allows Synthesis, Motion Captured Data