We describe a system which is designed to assist animators in extracting high-level information from sequences of images. The system is not meant to replace animators, but to be a tool to assist them in creating the first 'roughcut' of a sequence quickly and easily. Using the system, short animations have been created in a very short space of time. We show that the method of principal components analysis followed by a neural network learning phase is capable of motion tracking (even through occlusion), feature-extraction and gait classification. We quantify the results, and demonstrate the system tracking horses, birds and actors in a film. We demonstrate a system that is powerful, flexible and, above all, easy for non-specialists to use.
This paper presents a novel technique for the generation of 'video textures' to display human emotion. This is achieved by a method which uses existing video footage to synthesise new sequences of coherent facial expression and head motions.An 'expression space' which is defined by sets of emotion models is constructed using principal components analysis (PCA) and the application of an auto-regressive process (ARP). Using this expression space a tool has been developed which enables this space to be navigated, and to view new video sequences generated from it. This tool provides an intuitive interface, which allows a nonspecialist user easy traversal of the highly dimensional expression space and enables them to select an emotion and automatically generate new facial expression sequences.
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