Abstract. 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.
Okwechime and Bowden REF extended this work using a multivariate probability distribution to blend between different types of cyclic motion to create novel movement.
A generative model for motion synthesis and blending using probability density estimation
in Proc. 5th Conf. Articulated Motion and Deformable Objects, Mallorca
Computer Science