  Many classical parametric 3D shape models exist, but creating novel shapes
with such models requires expert knowledge of their parameters. For example,
imagine creating a specific type of tree using procedural graphics or a new
kind of animal from a statistical shape model. Our key idea is to leverage
language to control such existing models to produce novel shapes. This involves
learning a mapping between the latent space of a vision-language model and the
parameter space of the 3D model, which we do using a small set of shape and
text pairs. Our hypothesis is that mapping from language to parameters allows
us to generate parameters for objects that were never seen during training. If
the mapping between language and parameters is sufficiently smooth, then
interpolation or generalization in language should translate appropriately into
novel 3D shapes. We test our approach with two very different types of
parametric shape models (quadrupeds and arboreal trees). We use a learned
statistical shape model of quadrupeds and show that we can use text to generate
new animals not present during training. In particular, we demonstrate
state-of-the-art shape estimation of 3D dogs. This work also constitutes the
first language-driven method for generating 3D trees. Finally, embedding images
in the CLIP latent space enables us to generate animals and trees directly from
images.
