momo

Type Members

1. trait MoMo extends AnyRef

   

   3D Morphable Model with shape, color and expressions

2. case class MoMoBasic(referenceMesh: TriangleMesh3D, shape: PancakeDLRGP[_3D, UnstructuredPointsDomain[_3D], Point[_3D]], color: PancakeDLRGP[_3D, UnstructuredPointsDomain[_3D], RGB], landmarks: Map[String, Landmark[_3D]] = Map.empty[String, Landmark[_3D]]) extends MoMo with Product with Serializable

   

3. case class MoMoCoefficients(shape: DenseVector[Double], color: DenseVector[Double], expression: DenseVector[Double]) extends Product with Serializable

   

   coefficients describing a Morphable Model instance

4. case class MoMoExpress(referenceMesh: TriangleMesh3D, shape: PancakeDLRGP[_3D, UnstructuredPointsDomain[_3D], Point[_3D]], color: PancakeDLRGP[_3D, UnstructuredPointsDomain[_3D], RGB], expression: PancakeDLRGP[_3D, UnstructuredPointsDomain[_3D], EuclideanVector[_3D]], landmarks: Map[String, Landmark[_3D]]) extends MoMo with Product with Serializable

   

   3d Morphable Model implementation, includes facial expression

   3d Morphable Model implementation, includes facial expression

   The model consists of a shape and color model together with noise estimates. Both models are defined over points of the reference Mesh. The models are spherical PPCA models.

   referenceMesh

   reference of the model

   shape

   the shape model

   color

   the color model

5. case class PancakeDLRGP[D <: Dim, +DDomain <: DiscreteDomain[D], Value](gpModel: DiscreteLowRankGaussianProcess[D, DDomain, Value], noiseVariance: Double)(implicit evidence$1: NDSpace[D]) extends Product with Serializable

   

   Models of the type DiscreteLowRankGaussianProcess + noiseVariance * delta - extension of DLRGP to whole space (pancake) This is a PPCA model but the basis matrix does not need to be orthogonal

   Models of the type DiscreteLowRankGaussianProcess + noiseVariance * delta - extension of DLRGP to whole space (pancake) This is a PPCA model but the basis matrix does not need to be orthogonal

   D

   dimensionality of domain

   Value

   value type of model

   gpModel

   underlying discrete low-rank Gaussian Process, subspace model

   noiseVariance

   isotropic noise variance, "subspace + N(0, noiseVariance)"