  The shape of many objects in the built environment is dictated by their
relationships to the human body: how will a person interact with this object?
Existing data-driven generative models of 3D shapes produce plausible objects
but do not reason about the relationship of those objects to the human body. In
this paper, we learn body-aware generative models of 3D shapes. Specifically,
we train generative models of chairs, an ubiquitous shape category, which can
be conditioned on a given body shape or sitting pose. The
body-shape-conditioned models produce chairs which will be comfortable for a
person with the given body shape; the pose-conditioned models produce chairs
which accommodate the given sitting pose. To train these models, we define a
"sitting pose matching" metric and a novel "sitting comfort" metric.
Calculating these metrics requires an expensive optimization to sit the body
into the chair, which is too slow to be used as a loss function for training a
generative model. Thus, we train neural networks to efficiently approximate
these metrics. We use our approach to train three body-aware generative shape
models: a structured part-based generator, a point cloud generator, and an
implicit surface generator. In all cases, our approach produces models which
adapt their output chair shapes to input human body specifications.
