  For the task of mobility analysis of 3D shapes, we propose joint analysis for
simultaneous motion part segmentation and motion attribute estimation, taking a
single 3D model as input. The problem is significantly different from those
tackled in the existing works which assume the availability of either a
pre-existing shape segmentation or multiple 3D models in different motion
states. To that end, we develop Shape2Motion which takes a single 3D point
cloud as input, and jointly computes a mobility-oriented segmentation and the
associated motion attributes. Shape2Motion is comprised of two deep neural
networks designed for mobility proposal generation and mobility optimization,
respectively. The key contribution of these networks is the novel motion-driven
features and losses used in both motion part segmentation and motion attribute
estimation. This is based on the observation that the movement of a functional
part preserves the shape structure. We evaluate Shape2Motion with a newly
proposed benchmark for mobility analysis of 3D shapes. Results demonstrate that
our method achieves the state-of-the-art performance both in terms of motion
part segmentation and motion attribute estimation.
