  We present ShapeCrafter, a neural network for recursive text-conditioned 3D
shape generation. Existing methods to generate text-conditioned 3D shapes
consume an entire text prompt to generate a 3D shape in a single step. However,
humans tend to describe shapes recursively-we may start with an initial
description and progressively add details based on intermediate results. To
capture this recursive process, we introduce a method to generate a 3D shape
distribution, conditioned on an initial phrase, that gradually evolves as more
phrases are added. Since existing datasets are insufficient for training this
approach, we present Text2Shape++, a large dataset of 369K shape-text pairs
that supports recursive shape generation. To capture local details that are
often used to refine shape descriptions, we build on top of vector-quantized
deep implicit functions that generate a distribution of high-quality shapes.
Results show that our method can generate shapes consistent with text
descriptions, and shapes evolve gradually as more phrases are added. Our method
supports shape editing, extrapolation, and can enable new applications in
human-machine collaboration for creative design.
