Animation by Machine Learning with Motion Capture Data

Neil D. Lawrence

This project is about using Machine Learning to model human motion for animation, with a particular focus on the demands of computer games. The idea is to learn what natural motion looks like, and then combine it with constraints to develop an animation sequence. The constraints could be animator imposed, or imposed by the computer game. A typical scenario might be that the player’s character is required to interact with a character in the game, for example a player might be given an object in the game. The constraint could be that the hands of the player touch the hands of the character giving the object. With the current approach to animation (looking up a library of motion capture data) such a constraint is very difficult to fulfill as the required motion won’t exactly match a sequence in the library. By modelling natural motion through machine learning, we should be able to generate a new sequence to satisfy the constraint.

The models of motion will be developed using Gaussian processes. In particular the project will make use of the “Gaussian Process Latent Variable Model” (Lawrence, 2003) which has already shown a lot of promise in this domain, and “Latent Force Models” (Alvarez et al, 2009) a recently developed approach to learning based on physics and probabilistic models.

The project will involve a large amount of mathematics, in particular advanced linear algebra and calculus.