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Graph."],"published-print":{"date-parts":[[2017,12,31]]},"abstract":"<jats:p>\n            We introduce a method for learning a model for the\n            <jats:italic>mobility<\/jats:italic>\n            of parts in 3D objects. Our method allows not only to understand the dynamic functionalities of one or more parts in a 3D object, but also to apply the mobility functions to\n            <jats:italic>static<\/jats:italic>\n            3D models. Specifically, the learned part mobility model can\n            <jats:italic>predict<\/jats:italic>\n            mobilities for parts of a 3D object given in the form of a\n            <jats:italic>single static<\/jats:italic>\n            snapshot reflecting the spatial configuration of the object parts in 3D space, and\n            <jats:italic>transfer<\/jats:italic>\n            the mobility from relevant units in the training data. The training data consists of a set of\n            <jats:italic>mobility units<\/jats:italic>\n            of different motion types. Each unit is composed of a pair of 3D object parts (one moving and one reference part), along with usage examples consisting of a few snapshots capturing different motion states of the unit. Taking advantage of a linearity characteristic exhibited by most part motions in everyday objects, and utilizing a set of part-relation descriptors, we define a mapping from static snapshots to dynamic units. This mapping employs a\n            <jats:italic>motion-dependent<\/jats:italic>\n            snapshot-to-unit distance obtained via metric learning. We show that our learning scheme leads to accurate motion prediction from single static snapshots and allows proper motion transfer. We also demonstrate other applications such as motion-driven object detection and motion hierarchy construction.\n          <\/jats:p>","DOI":"10.1145\/3130800.3130811","type":"journal-article","created":{"date-parts":[[2017,11,22]],"date-time":"2017-11-22T16:25:08Z","timestamp":1511367908000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Learning to predict part mobility from a single static snapshot"],"prefix":"10.1145","volume":"36","author":[{"given":"Ruizhen","family":"Hu","sequence":"first","affiliation":[{"name":"Shenzhen University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenchao","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oliver","family":"Van Kaick","sequence":"additional","affiliation":[{"name":"Carleton University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ariel","family":"Shamir","sequence":"additional","affiliation":[{"name":"The Interdisciplinary Center"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,11,20]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2601097.2601185"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1136800"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1057432.1057461"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995327"},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925939"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cag.2013.05.020"},{"key":"e_1_2_2_7_1","volume-title":"Int. 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