  Parametric CAD models encode entire families of shapes that should, in
principle, be easy for designers to explore. However, in practice, parametric
CAD models can be difficult to manipulate due to implicit semantic constraints
among parameter values. Finding and enforcing these semantic constraints solely
from geometry or programmatic shape representations is not possible because
these constraints ultimately reflect design intent. They are informed by the
designer's experience and semantics in the real world. To address this
challenge, we introduce a zero-shot pipeline that leverages pre-trained large
language and image model to infer meaningful space of variations for a shape.
We then re-parameterize a new constrained parametric CAD program that captures
these variations, enabling effortless exploration of the design space along
meaningful design axes.
