A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Multilinear Analysis of Image Ensembles: TensorFaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
The space of human body shapes: reconstruction and parameterization from range scans
ACM SIGGRAPH 2003 Papers
SCAPE: shape completion and animation of people
ACM SIGGRAPH 2005 Papers
Face transfer with multilinear models
ACM SIGGRAPH 2005 Papers
Creating face models from vague mental images
SIGGRAPH '05 ACM SIGGRAPH 2005 Sketches
Implicit Meshes for Effective Silhouette Handling
International Journal of Computer Vision
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
Facial type, expression, and viseme generation
ACM SIGGRAPH 2007 posters
Data-driven enhancement of facial attractiveness
ACM SIGGRAPH 2008 papers
Face modeling and editing with statistical local feature control models
International Journal of Imaging Systems and Technology
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Generative 3D Face Models are expressive models with applications in modelling and editing. They are learned from example faces, and offer a compact representation of the continuous space of faces. While they have proven to be useful as strong priors in face reconstruction they remain to be difficult to use in artistic editing tasks. We describe a way to navigate face space by changing meaningful parameters learned from the training data. This makes it possible to fix attributes such as height, weight, age, expression or `lack of sleep' while letting the infinity of unfixed other attributes vary in a statistically meaningful way. We propose an inverse approach based on learning the distribution of faces in attribute space. Given a set of target attributes we then find the face which has the target attributes with high probability, and is as similar as possible to the input face.