  In this paper, we compare methods for estimating the complexity of
two-dimensional shapes and introduce a method that exploits reconstruction loss
of Variational Autoencoders with different sizes of latent vectors. Although
complexity of a shape is not a well defined attribute, different aspects of it
can be estimated. We demonstrate that our methods captures some aspects of
shape complexity. Code and training details will be publicly available.
