  Learning a model of dynamics from high-dimensional images can be a core
ingredient for success in many applications across different domains,
especially in sequential decision making. However, currently prevailing methods
based on latent-variable models are limited to working with low resolution
images only. In this work, we show that some of the issues with using
high-dimensional observations arise from the discrepancy between the
dimensionality of the latent and observable space, and propose solutions to
overcome them.
