Introduction​

Generative models aim to transform a simple base distribution, such as a Gaussian, into a complex target data distribution. The core idea is to construct a generator that maps samples from the base distribution into data space. To compute likelihoods, this generator must be invertible. If a generator transforms $Z$ into $X = G(Z)$, then $Z = G^{-1}(X)$ must exist, and the density is computed using the change of variables:

$P_X(X) = P_Z(G^{-1}(X)) \left|\det(\partial G^{-1}(X)/\partial X)\right|$

Geometric Transformations​

The geometry of a mesh is defined by the vertex set Applying a transformation to a mesh means transforming each vertex location Simple transformations can be accomplished by multiplying a matrix times the vertex location

• Rotation
• Scale
• Translation

Integrated Writing​

Template1​

安装 Brew​

brew

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

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