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From Deformations to Parts: Motion-based Segmentation of 3D Objects
Soumya Ghosh · Erik Sudderth · Matthew Loper · Michael J Black

Tue Dec 04 07:00 PM -- 12:00 AM (PST) @ Harrah’s Special Events Center 2nd Floor

We develop a method for discovering the parts of an articulated object from aligned meshes capturing various three-dimensional (3D) poses. We adapt the distance dependent Chinese restaurant process (ddCRP) to allow nonparametric discovery of a potentially unbounded number of parts, while simultaneously guaranteeing a spatially connected segmentation. To allow analysis of datasets in which object instances have varying shapes, we model part variability across poses via affine transformations. By placing a matrix normal-inverse-Wishart prior on these affine transformations, we develop a ddCRP Gibbs sampler which tractably marginalizes over transformation uncertainty. Analyzing a dataset of humans captured in dozens of poses, we infer parts which provide quantitatively better motion predictions than conventional clustering methods.

Author Information

Soumya Ghosh (MIT-IBM Watson AI Lab, IBM Research)
Erik Sudderth (University of California, Irvine)
Matthew Loper (Max Planck Institute for Intelligent Systems)
Michael J Black (Max Planck Institute for Intelligent Systems)

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