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From Deformations to Parts: Motion-based Segmentation of 3D Objects

2012

Conference Paper

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We develop a method for discovering the parts of an articulated object from aligned meshes of the object in various three-dimensional 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 3D 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 deformation predictions than conventional clustering methods.

Author(s): Ghosh, Soumya and Sudderth, Erik and Loper, Matthew and Black, Michael
Book Title: Advances in Neural Information Processing Systems 25 (NIPS)
Pages: 2006--2014
Year: 2012
Editors: P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger
Publisher: MIT Press

Department(s): Perceiving Systems
Research Project(s): Part-based Body Models
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

URL: http://books.nips.cc/papers/files/nips25/NIPS2012_0989.pdf

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BibTex

@inproceedings{Ghosh:NIPS:2012,
  title = {From Deformations to Parts: Motion-based Segmentation of {3D} Objects },
  author = {Ghosh, Soumya and Sudderth, Erik and Loper, Matthew and Black, Michael},
  booktitle = {Advances in Neural Information Processing Systems 25 (NIPS)},
  pages = {2006--2014},
  editors = {P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger},
  publisher = {MIT Press},
  year = {2012},
  url = {http://books.nips.cc/papers/files/nips25/NIPS2012_0989.pdf}
}