  Existing methods of level generation using latent variable models such as
VAEs and GANs do so in segments and produce the final level by stitching these
separately generated segments together. In this paper, we build on these
methods by training VAEs to learn a sequential model of segment generation such
that generated segments logically follow from prior segments. By further
combining the VAE with a classifier that determines whether to place the
generated segment to the top, bottom, left or right of the previous segment, we
obtain a pipeline that enables the generation of arbitrarily long levels that
progress in any of these four directions and are composed of segments that
logically follow one another. In addition to generating more coherent levels of
non-fixed length, this method also enables implicit blending of levels from
separate games that do not have similar orientation. We demonstrate our
approach using levels from Super Mario Bros., Kid Icarus and Mega Man, showing
that our method produces levels that are more coherent than previous latent
variable-based approaches and are capable of blending levels across games.
