{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/from-deformations-to-parts-motion-based","title":"From Deformations to Parts: Motion-based Segmentation of 3D Objects","arxiv_id":null,"date":"2012-12-01","proceeding":"NeurIPS 2012 12","authors":["Soumya Ghosh","Matthew Loper","Erik B. Sudderth","Michael J. Black"],"abstract":"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.","url_abs":"http://papers.nips.cc/paper/4749-from-deformations-to-parts-motion-based-segmentation-of-3d-objects","url_pdf":"http://papers.nips.cc/paper/4749-from-deformations-to-parts-motion-based-segmentation-of-3d-objects.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"from-deformations-to-parts-motion-based","repo_url":"https://github.com/SoumyaTGhosh/ddcrpMeshSeg","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"from-deformations-to-parts-motion-based","repo_url":"https://github.com/ishanashastri/py-ddcrpMeshSeg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"object","task_name":"Object"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}