Abstract
We describe a generative model of ‘talking head’ facial behaviour, intended for use in both video synthesis and model-based interpretation. The model is learnt, without supervision, from talking head video, parameterised by tracking with an Active Appearance Model (AAM). We present a integrated probabilistic framework for capturing both the short-term visual dynamics and longer-term behavioural structure. We demonstrate that the approach leads to a compact model, capable of generating realistic and relatively subtle talking head behaviour in real time. The results of a forcedchoice psychophysical experiment show that the quality of the generated sequences is significantly better than that obtained using alternative approaches, and is indistinguishable from that of the original training sequence.
Paper version not known (
Free)
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call