3D MORPHABLE FACE MODELS REVISITED PDF

We demonstrate how the statistical tools of thin-plate splines and Procrustes analysis can be used to construct a morphable model that is both more ef? We also reformulate the probabilistic prior that the model provides on the distribution of parameter vector lengths. This distribution is determined solely by the number of model dimensions and can be used as a regularisation constraint in? As an example application of this improved model, we show how it may be? This provides a rapid means to estimate high resolution 3D face shape for a face in any pose given only a single face image.

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William A. BronsteinMichael M. Topics Discussed in This Paper. Their combined citations are counted only for the first article.

Face recognition based on frontal views generated from non-frontal images Volker BlanzPatrick GrotherP. New articles by this author. Skip to search form Skip to main content. Semantic Scholar estimates that this publication has 81 citations based on the available data. We demonstrate how the statistical tools of thin-plate splines and Procrustes analysis can be used to construct a morphable model that is both more efficient and generalises to novel face surfaces more accurately than previous models.

A library for approximate morels neighbor searching Image Processingpp. Showing of 51 extracted citations. Procrustes analysis Ground truth Thin plate spline. FitzgibbonWhat Shape Are Dolphins? Fitting a 3D morphable model to edges: Mdoels analysis Search for additional papers on this topic.

XiThree-dimensional human shape inference from silhouettes: GrahamActive shape models? SweldensThe Lifting Scheme: This paper has 81 citations.

Sparse language Plausibility structure Image resolution. Verified email at york. Email address for updates. Fully automatic registration and statistical analysis, Computer Vision and Image Understandingpp.

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3D MORPHABLE FACE MODELS REVISITED PDF

PY - Y1 - N2 - In this paper we revisit the process of constructing a high resolution 3D morphable model of face shape variation. We demonstrate how the statistical tools of thin-plate splines and Procrustes analysis can be used to construct a morphable model that is both more efficient and generalises to novel face surfaces more accurately than previous models. We also reformulate the probabilistic prior that the model provides on the distribution of parameter vector lengths. This distribution is determined solely by the number of model dimensions and can be used as a regularisation constraint in fitting the model to data without the need to empirically choose a parameter controlling the trade off between plausibility and quality of fit. As an example application of this improved model, we show how it may be fitted to a sparse set of 2D feature points approximately

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We demonstrate how the statistical tools of thin-plate splines and Procrustes analysis can be used to construct a morphable model that is both more efficient and generalises to novel face surfaces more accurately than previous models. We also reformulate the probabilistic prior that the model provides on the distribution of parameter vector lengths. This distribution is determined solely by the number of model dimensions and can be used as a regularisation constraint in fitting the model to data without the need to empirically choose a parameter controlling the trade off between plausibility and quality of fit. As an example application of this improved model, we show how it may be fitted to a sparse set of 2D feature points approximately This provides a rapid means to estimate high resolution 3D face shape for a face in any pose given only a single face image.

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William A. BronsteinMichael M. Topics Discussed in This Paper. Their combined citations are counted only for the first article. Face recognition based on frontal views generated from non-frontal images Volker BlanzPatrick GrotherP.

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