  The practice of transforming raw data to a feature space so that inference
can be performed in that space has been popular for many years. Recently, rapid
progress in deep neural networks has given both researchers and practitioners
enhanced methods that increase the richness of feature representations, be it
from images, text or speech. In this work we show how a constructed latent
space can be explored in a controlled manner and argue that this complements
well founded inference methods. For constructing the latent space a Variational
Autoencoder is used. We present a novel controller module that allows for
smooth traversal in the latent space and construct an end-to-end trainable
framework. We explore the applicability of our method for performing spatial
transformations as well as kinematics for predicting future latent vectors of a
video sequence.
