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Statistical Analysis of 3D Faces in Motion
We perform statistical analysis of 3D facial shapes in motion over different subjects and different motion sequences. For this, we represent each motion sequence in a multilinear model space using one vector of coefficients for identity and one high-dimensional curve for the motion. We apply the resulting statistical model to two applications: to synthesize motion sequences, and to perform expression recognition. En route to building the model, we present a fully automatic approach to register 3D facial motion data, based on a multilinear model, and show that the resulting registrations are of high quality.
@inproceedings{BolkartWuhrere_3DV_2013, title = {Statistical Analysis of 3D Faces in Motion}, booktitle = {International Conference on 3D Vision (3DV) }, abstract = {We perform statistical analysis of 3D facial shapes in motion over different subjects and different motion sequences. For this, we represent each motion sequence in a multilinear model space using one vector of coefficients for identity and one high-dimensional curve for the motion. We apply the resulting statistical model to two applications: to synthesize motion sequences, and to perform expression recognition. En route to building the model, we present a fully automatic approach to register 3D facial motion data, based on a multilinear model, and show that the resulting registrations are of high quality.}, pages = {103--110}, month = jun, year = {2013}, slug = {bolkartwuhrere_3dv_2013}, author = {Bolkart, Timo and Wuhrer, Stefanie}, month_numeric = {6} }