Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Real-time estimation of long-term 3-D motion parameters for SNHC face animation and model-based coding applications

: Smolic, A.; Makai, B.; Sikora, T.


IEEE transactions on circuits and systems for video technology 9 (1999), Nr.2, S.255-263
ISSN: 1051-8215
ISSN: 1558-2205
Fraunhofer HHI ()
computer animation; feature extraction; filtering theory; image coding; image sequences; kalman filters; least squares approximations; motion estimation; nonlinear filters; prediction theory; recursive estimation; long-term 3d motion parameters; snhc face animation; model-based coding applications; real-time estimation; monocular image sequences; synthetic/natural hybrid coding; feature point extraction; energy frame; predictive approach; recursive linear least squares; nonlinear extended kalman filter; correction loop; prediction loop; error accumulation; long-term motion estimation; experimental results; synthetic data; real image sequences

We present two recursive methods for the real-time estimation of long-term three-dimensional (3-D) motion parameters from monocular image sequences suitable for synthetic/natural hybrid coding face animation and model-based coding applications. Based on feature point extractions in energy frame, the 3-D motion parameters of a human face are estimated with a predictive approach. The first method uses a recursive linear least squares approach and the second employs a nonlinear extended Kalman filter, which does not rely on a linearized model of the face motion. Both methods perform a prediction and correction loop at every time step. Compared to other methods described in the literature, the recursive and predictive structure of the proposed estimation process solves the p