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It allows us to understand the shape variation of each shape in the context of neighboring shapes and also offers structure-preserving interpolations between the input shapes. We show how to extract these shape variations by recovering piecewise affine vector fields in the tangent space of each shape. These vector fields provide single-shape segmentation cues. We then derive shape correspondences by iteratively propagating AAAP deformations across a sequence of intermediate shapes. These correspondences are then used to aggregate single-shape segmentation cues into consistent segmentations. We conduct experiments on the ShapeNet dataset to show superior performance in shape matching and joint shape segmentation over previous methods.<\/jats:p>","DOI":"10.1145\/3731164","type":"journal-article","created":{"date-parts":[[2025,7,27]],"date-time":"2025-07-27T04:02:22Z","timestamp":1753588942000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["GenAnalysis: Joint Shape Analysis by Learning Man-Made Shape Generators with Deformation Regularizations"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8814-2095","authenticated-orcid":false,"given":"Yuezhi","family":"Yang","sequence":"first","affiliation":[{"name":"University of Texas at Austin, Austin, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9992-6387","authenticated-orcid":false,"given":"Haitao","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Texas at Austin, Austin, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9375-9822","authenticated-orcid":false,"given":"Kiyohiro","family":"Nakayama","sequence":"additional","affiliation":[{"name":"Stanford University, Palo Alto, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9533-9546","authenticated-orcid":false,"given":"Xiangru","family":"Huang","sequence":"additional","affiliation":[{"name":"Westlake University, HangZhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8315-4886","authenticated-orcid":false,"given":"Leonidas","family":"Guibas","sequence":"additional","affiliation":[{"name":"Stanford University, Palo Alto, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7908-7174","authenticated-orcid":false,"given":"Qixing","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Texas at Austin, Austin, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,7,27]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01392"},{"key":"e_1_2_2_2_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsm\u00e4ssan","author":"Achlioptas Panos","year":"2018","unstructured":"Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas J. 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