Abstract. Genetic Programming techniques can be used to produce regression equations that quantify emotional expressions on a facial model. The formulae give emotional scores based on the position of 25 automatically generated "landmarks" on the face. The method shown here is an integrated part of a system that maps multidimensional data sets to naturalistic visual structures such as a face.

This paper demonstrates a technique for visualising multidimensional data sets holistically. The EVA algorithm provides an automatic mapping from semantically important features of the data to emotionally or perceptually significant features of the corresponding visual structure (such as a face). In other words a single glance at the visual structure informs the observer of the global state of the data, since the visual structure has an emotional impact on the observer. The visualisation is designed to correspond to the impact that would have been generated had the observer been able to access the relevant semantics of the underlying data.

No abstract

Computer Animation, has advanced significantly as well as spectacularly over the last two-to-three decades. The interesting field has innovated tremendously moving from first-generation i.e. purely geometric models where motion was developed using kinematic techniques, to secondgeneration i.e. physical-based models (direct versus inverse dynamics and hybrid systems), towards the development of the third-generation i.e. autonomous behaviour through learning and perception. The simulation and animation of a variety of real-world objects appear with stunning realism. However, the main issue here is not on what we have achieved but rather, based on what we have achieved, what is to follow. The emerging results are expected immensely by researchers in the field, worldwide. The paper delivers a survey on human figure animation and in particular a classification framework of the work done on the control of locomotion.