The group has a long track record of games technology related research, as well as extensive experience within the games industry.
The central themes of the research group are twofold:
A number of funded projects provide the backbone for our academic research, which result in a regular output of publications.
Current areas of research include:
Physics-Based Synthetic Character Motion. We aim to develop natural, realistic character motion for simulation in real-time environments (i.e. games). Physics-based methods enable us to create characters that react to dynamically changing environments (e.g. being pushed). We achieve this by using various approaches, such as the inverted pendulum model for autonomous balancing, and motion capture data for style (e.g. tired walk, sad walk). Behavioural effects are used in combination with physically correct full body models, to generate real-world reactions to disturbances.
Using video game physics engine technology to perform simulations of real-world engineering scenarios. The idea is to use technology developed for the games industry to create real-time simulations for use in early design and prototyping stages, instead of the more accurate but complex and time-consuming Finite Element Analysis systems that are used at the moment. Currently, focusing on simulating trains running on tracks in order to look into the effects of crashes and explosions.
A touch-free interactive storytelling prototype for neurologically challenged children on the Android mobile platform. We have been exploring potential avenues and currently are working on computer vision techniques (gesture recognition via custom colour tracking) to help the children with their rehabilitation; also, for extreme cases, we are considered blowing into the device microphone.
A predicative model based on application semantics that can be used in optimistic replication to reduce inconsistency arising from transactions. We are investigating a probabilistic approach which predicts the potential future state progression of a client. This approach provides the server with probabilistic information to reduce the number of aborted transactions during reconciliation.