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Articulated objects are pervasive in daily life. However, due to theintrinsic high-DoF structure, the joint states of the articulated objects arehard to be estimated. To model articulated objects, two kinds of shapedeformations namely the geometric and the pose deformation should beconsidered. In this work, we present a novel category-specific parametricrepresentation called Object Model with Articulated Deformations (OMAD) toexplicitly model the articulated objects. In OMAD, a category is associatedwith a linear shape function with shared shape basis and a non-linear jointfunction. Both functions can be learned from a large-scale object model datasetand fixed as category-specific priors. Then we propose an OMADNet to predictthe shape parameters and the joint states from an object's single observation.With the full representation of the object shape and joint states, we canaddress several tasks including category-level object pose estimation and thearticulated object retrieval. To evaluate these tasks, we create a syntheticdataset based on PartNet-Mobility. Extensive experiments show that our simpleOMADNet can serve as a strong baseline for both tasks.

OMAD: Object Model with Articulated Deformations for Pose Estimation and Retrieval