Generative Modelling for Sequenatial Human Behaviour

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The goal of this research is to analyze and model sequential human behaviour such as speech,facial animation and gestures. The first step towards achieving this is to identify the factors thatcontribute and guide human behaviour. Generative models can reveal hidden structures in thedata which are often referred to as the latent representation of the data. As part of my researchI want to focus on Finding ways to disentangle these latent variables in order to gain control overdifferent aspects the generated sequences.I would like to explore recent generative methods and adapt them to handle sequential data.One such model is generative adversarial networks, which uses a discriminating network to drive thelearning of a generating network. This approach has been very successful for generating static dataand its extension to sequences is a very active area of research. Another advantage of exploringthis approach is that it allows the use of multiple discriminator networks that are capable ofsimultaneously capturing various aspects of real human behavioural data.Another goal of this research is to understand the relationship between the signals that make uphuman behaviour because these signals are often linked. A good example of this is the correlationbetween human speech and facial animation. I plan to exploit this relationship to perform speech-driven animation which will greatly reduce the cost of computer generated imagery (CGI).Additionally, I believe that it is important to research methods that model the changes in signalsrather than the signals themselves. Such models may be better suited to capture the dynamics ofmost natural systems, hence I would like to research new network architectures that make theseapproaches possible.Finally as we seek to constantly improve the realism of generated human behavioural data itis also important to find ways to distinguish generated signals from real ones. Generating contentthat is very realistic can have great security implications (i.e. identity theft) and therefore I wouldalso like to explore ways of distinguishing real and generated human behavioural data.This research is in line with the goals of EPSRC in the fields of computer graphics and human-computer interaction since it will enable the fast and efficient generation of animated characters.