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  • MAGE Group

    Modelling Animation Games and Effects Group 

  • About the group

    About the group

    MAGE are exploring emerging techniques looking to the next generation of computer animation systems. Our research includes functionally based heterogeneous modelling, volumetric modelling for games, and serious games with advanced intelligence. For a brief introduction to some of our projects see below. More details can be found by looking at individual Researchers or our Publications page. The head of the group is  Prof. Peter Comninos.

  • HyperFun


    HyperFun is an international, collaborative, open source software project.

    Its goal is to provide new software tools to enable precise compact volumetric modelling. Complex shapes are created by operating on simple shapes, making life much easier for the animator. Outside of games and animation HyperFun has applications in design and cultural heritage preservation.

  • C-Sheep


    The C-Sheep system is an educational tool that teaches programming and computer science principles.  Users control a roller-skating sheep inside a virtual meadow with a command syntax drawn from a subset of C. The use of algorithms and effects from commercial games is to attract students who may normally avoid the scary world of programming.

  • Augmented sculpture

    Augmented sculpture

    Uses f-rep to model existing sculptures in a virtual environment. Sculptures with inherent dynamic potential are chosen and then animated.

    The project aims at the creation of a time dependent interactive space where users can ‘carve’ morph sculptures on the fly. In collaboration with Russian artist Igor Seleznev we aim to ‘close the circle’ and bring a selection of these virtual sculptures into the physical world.
  • Dynamic weather

    Dynamic weather

    This project is aimed at producing fully dynamic weather and atmospheric effects for game-like virtual environments. This image is taken from an animation sequence from a 2D vortex-based fluid dynamics simulation that uses a hybrid CPU-GPU approach to trace the flow using over half a million particles.