Recover Head Poses by Reducing the Dimension
Values obtained after dimensionality reduction can be interpreted as latent variables of a generative process for the data. In some cases, these latent variables have simple interpretations. In this example, it can be seen how the orientation of 3D objects can be discovered from 2D projections of these objects.
Generate a dataset of different head poses from 3D geometry data with random viewpoints.
Visualize different head poses.
Reduce the dataset to a two-dimensional representation using the local linear embedding method.
Visualize the original images in the reduced space, in which the up-down and front-side poses are disentangled.