Fast Deformable Object Simulation for VR and Computer Games (SUSTech candidates only)
This project is available as part of the Leeds-SUSTech Split-Site PhD Programme.
Deformable object simulation such fabrics, cloths, ragdolls and fluid has been a popular research area in the past two decades. The applications range from computer graphics, animation, robotics, design to Virtual Reality (VR). On the one hand, stable and perceptually realistic simulation methods have been proposed over the years; on the other hand, none of them can run fast enough in emerging media such as VR (which requires 90-120Hz frame rate). The bottlenecks lie mainly in solving the underlying dynamic equations and resolving collisions. As fast progresses being made in high performance computing and machine learning, these bottlenecks could be addressed by parallel collision detection and data-driven physical models. While the parallel collision handling provides fast detection and resolution, data-driven approaches could simplify the underlying physical mechanism. This PhD project is to propose new data-driven simulation methods and fast continuous collision detection approaches to make simulations of deformable objects fast enough to be used in demanding situations such as VR.
Applications are invited from candidates with or expecting the minimum equivalent of a UK upper second class honours degree (2:1), and/or a Masters degree in computing or a related discipline. If English is not your first language, you must provide evidence that you have English language proficiency of at least IELTS 6.5 with no component below 6.0, or equivalent.
How to apply
Applications should be submitted via SUSTech in the first instance. Following nomination by SUSTech, formal applications for Split-site research degree study should then be made online through the University of Leeds website. Please state clearly in the research information section that the PhD you wish to be considered for is ‘Fast Deformable Object Simulation for VR and Computer Games (SUSTech candidates only)’ as well as Dr He Wang as your proposed supervisor.
If you require any further information please contact the Graduate School Office
e: email@example.com, t: +44 (0)113 343 8000.