  Articulated objects are pervasive in daily life. However, due to the
intrinsic high-DoF structure, the joint states of the articulated objects are
hard to be estimated. To model articulated objects, two kinds of shape
deformations namely the geometric and the pose deformation should be
considered. In this work, we present a novel category-specific parametric
representation called Object Model with Articulated Deformations (OMAD) to
explicitly model the articulated objects. In OMAD, a category is associated
with a linear shape function with shared shape basis and a non-linear joint
function. Both functions can be learned from a large-scale object model dataset
and fixed as category-specific priors. Then we propose an OMADNet to predict
the shape parameters and the joint states from an object's single observation.
With the full representation of the object shape and joint states, we can
address several tasks including category-level object pose estimation and the
articulated object retrieval. To evaluate these tasks, we create a synthetic
dataset based on PartNet-Mobility. Extensive experiments show that our simple
OMADNet can serve as a strong baseline for both tasks.
