Computer Graphics and Virtual Reality Lab

Department of Computer Science

Dr Andreas Aristidou

Research Interests

My main interests are focused on 3D Motion Analysis and involves Optical Motion Capture, Real Time Marker prediction and CoR estimation, Inverse Kinematics and Applications of Geometric Algebra in Engineering.


In this project, a dancing performer will interact with a virtual animated character, in real-time, to compose a contemporary dancing show. The proposed research will explore innovative topics with special interest in the area of computer animation, including methods which smoothly combine optical motion capture (mocap) data with kinematic techniques, human figure modelling, a novel methodology for motion classification and partial-body motion synthesis. The system will be adjusted dynamically according to the performers' actions and responses, offering the maximum possible interaction between the natural and virtual performer. Similar techniques can be adapted to the game industry, possibly for military or local law enforcement training simulators or other virtual character animations.



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This project aims to create a publicly accessible digital archive of folk dances using 3D motion capture data (with metadata); currently, only 2D video recordings are used to document traditional Cypriot dance performances, having although restrictions (mainly due to the character occlusions) by the limited capabilities of the 2D cameras. In addition to rare video material held by local cultural institutions, state-of-the-art motion capture technologies are utilized to record and archive high quality motion data of expert dancers performing these traditional dances. Apart from the goal of preserving this intangible cultural heritage by digitizing it, the project is interested in increasing the awareness of the local community to its dance heritage. To achieve this a 3D video game for children is developed to teach these folk dances to the younger generations.




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This work addresses the problem of real-time joint localisation of legged skeletons in the presence of missing data. The data is assumed to be labelled 3d marker positions from a motion capture system.n integrated framework is presented which predicts the occluded marker positions using a variable turn model within an Unscented Kalman filter. Inferred information from neighbouring markers is used as observation states; these constraints are efficient, simple and real-time implementable. This work also takes advantage of the common case that missing markers are often visible to only a single camera, resulting in more accurate predictions. An Inverse Kinematics technique is then applied ensuring that the bone lengths remain constant over time; the system can thereby maintain a continuous data-flow. The marker and Centre of Rotation (CoR) positions can be calculated with high accuracy even in cases where markers are occluded for a long period of time. Our methodology is tested against some of the most popular methods for marker prediction and the results confirm that our approach outperforms these methods in estimating both marker and CoR positions.





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Inverse Kinematics is defined as the problem of determining a set of appropriate joint configurations for which the end effectors move to desired positions as smoothly, rapidly, and as accurately as possible. However, many of the currently available methods suffer from high computational cost and production of unrealistic poses. In this work, a novel heuristic method, called Forward And Backward Reaching Inverse Kinematics (FABRIK), is described and compared with some of the most popular existing methods regarding reliability, computational cost and conversion criteria. FABRIK avoids the use of rotational angles or matrices, and instead finds each joint position via locating a point on a line. Thus, it converges in fewer iterations, has low computational cost and produces visually realistic poses. Constraints can easily be incorporated within FABRIK and multiple chains with multiple end effectors are also easily supported.

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Articulated hand tracking systems have been commonly used in virtual reality applications, including systems with human-computer interaction or interaction with game consoles. However, building an effective real-time hand pose tracker remains challenging. In this work, we present a simple and efficient methodology for tracking and reconstructing 3d hand poses using a markered optical motion capture system. Markers were positioned at strategic points, and an inverse kinematics solver (FABRIK) was incorporated to fit the rest of the joints to the hand model. The model is highly constrained with physiological constraints, allowing motion only within a feasible set. The method is real-time implementable and the results are smooth, even with a low frame rate.


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Geometric Algebra (GA) provides a convenient mathematical notation for representing orientations and rotations of objects in three dimensions. The Conformal model of GA give us the ability to describe algorithms in a geometrically intuitive and compact manner since basic entities, such as spheres, lines, planes and circles, are simply represented by algebraic objects. GA is also more numerically stable and more efficient than rotation matrices making it popular for applications in computer graphics and robotics.







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