  Autoregressive models have proven to be very powerful in NLP text generation
tasks and lately have gained popularity for image generation as well. However,
they have seen limited use for the synthesis of 3D shapes so far. This is
mainly due to the lack of a straightforward way to linearize 3D data as well as
to scaling problems with the length of the resulting sequences when describing
complex shapes. In this work we address both of these problems. We use octrees
as a compact hierarchical shape representation that can be sequentialized by
traversal ordering. Moreover, we introduce an adaptive compression scheme, that
significantly reduces sequence lengths and thus enables their effective
generation with a transformer, while still allowing fully autoregressive
sampling and parallel training. We demonstrate the performance of our model by
comparing against the state-of-the-art in shape generation.
