  Given a 3D object, kinematic motion prediction aims to identify the mobile
parts as well as the corresponding motion parameters. Due to the large
variations in both topological structure and geometric details of 3D objects,
this remains a challenging task and the lack of large scale labeled data also
constrain the performance of deep learning based approaches. In this paper, we
tackle the task of object kinematic motion prediction problem in a semi-weakly
supervised manner. Our key observations are two-fold. First, although 3D
dataset with fully annotated motion labels is limited, there are existing
datasets and methods for object part semantic segmentation at large scale.
Second, semantic part segmentation and mobile part segmentation is not always
consistent but it is possible to detect the mobile parts from the underlying 3D
structure. Towards this end, we propose a graph neural network to learn the map
between hierarchical part-level segmentation and mobile parts parameters, which
are further refined based on geometric alignment. This network can be first
trained on PartNet-Mobility dataset with fully labeled mobility information and
then applied on PartNet dataset with fine-grained and hierarchical part-level
segmentation. The network predictions yield a large scale of 3D objects with
pseudo labeled mobility information and can further be used for
weakly-supervised learning with pre-existing segmentation. Our experiments show
there are significant performance boosts with the augmented data for previous
method designed for kinematic motion prediction on 3D partial scans.
