Fitting 3D Morphable Models using Local Features
This addresses the challenge of robust 3D face modeling under varying imaging conditions, though it appears incremental as it builds on existing cascaded regression techniques.
The paper tackles the problem of fitting 3D Morphable Models to 2D images by proposing a novel method that uses local features for improved robustness, achieving applicability for real-time applications.
In this paper, we propose a novel fitting method that uses local image features to fit a 3D Morphable 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 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 (https://github.com/patrikhuber).