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Fitting 3D morphable face models using local features

  • In this paper, we propose a novel fitting method that uses local image features to fit a 3D morphable face model to 2D images. To overcome the obstacle of optimising a cost function that contains a non-differentiable feature extraction operator, we use a learning-based cascaded regression method that learns the gradient direction from data. The method allows to simultaneously solve for shape and pose parameters. Our method is thoroughly evaluated on morphable model generated data and first results on real data are presented. Compared to traditional fitting methods, which use simple raw features like pixel colour or edge maps, local features have been shown to be much more robust against variations in imaging conditions. Our approach is unique in that we are the first to use local features to fit a 3D morphable model. Because of the speed of our method, it is applicable for realtime applications. Our cascaded regression framework is available as an open source library at github.com/patrikhuber/ superviseddescent.

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Metadaten
Name:Rätsch, Matthias
DOI:https://doi.org/10.1109/ICIP.2015.7350989
ISBN:978-1-4799-8339-1
Erschienen in:2015 IEEE International Conference on Image Processing (ICIP) : 27 - 30 Sept. 2015, Québec City, Canada
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Language:English
Year of Publication:2015
Tag:3D morphable model; 3D reconstruction; cascaded regression; supervised descent
Pagenumber:5
First Page:1195
Last Page:1199
Dewey Decimal Classification:006 Spezielle Computerverfahren
Open Access:Nein
Licence (German):License Logo  Lizenzbedingungen IEEE