{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:17:44Z","timestamp":1771697864635,"version":"3.50.1"},"reference-count":20,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2015,7,27]],"date-time":"2015-07-27T00:00:00Z","timestamp":1437955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2015,7,27]]},"abstract":"<jats:p>\n            We present a method to learn and propagate\n            <jats:italic>shape placements<\/jats:italic>\n            in 2D polygonal scenes from a few examples provided by a user. The placement of a shape is modeled as an oriented bounding box. Simple geometric relationships between this bounding box and nearby scene polygons define a\n            <jats:italic>feature set<\/jats:italic>\n            for the placement. The feature sets of all example placements are then used to learn a probabilistic model over all possible placements and scenes. With this model, we can generate a new set of placements with similar geometric relationships in any given scene. We introduce extensions that enable propagation and generation of shapes in 3D scenes, as well as the application of a learned modeling\n            <jats:italic>session<\/jats:italic>\n            to large scenes without additional user interaction. These concepts allow us to generate complex scenes with thousands of objects with relatively little user interaction.\n          <\/jats:p>","DOI":"10.1145\/2766933","type":"journal-article","created":{"date-parts":[[2015,7,28]],"date-time":"2015-07-28T12:26:38Z","timestamp":1438086398000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Learning shape placements by example"],"prefix":"10.1145","volume":"34","author":[{"given":"Paul","family":"Guerrero","sequence":"first","affiliation":[{"name":"KAUST, Vienna University of Technology"}]},{"given":"Stefan","family":"Jeschke","sequence":"additional","affiliation":[{"name":"IST Austria"}]},{"given":"Michael","family":"Wimmer","sequence":"additional","affiliation":[{"name":"Vienna University of Technology"}]},{"given":"Peter","family":"Wonka","sequence":"additional","affiliation":[{"name":"KAUST"}]}],"member":"320","published-online":{"date-parts":[[2015,7,27]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Pattern Recognition and Machine Learning (Information Science and Statistics)","author":"Bishop C. 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Pedersen P. S. Arildsen T. and Larsen T. 2012. Improving smoothed l0 norm in compressive sensing using adaptive parameter selection. CoRR abs\/1210.4277.  Oxvig C. S. Pedersen P. S. Arildsen T. and Larsen T. 2012. Improving smoothed l0 norm in compressive sensing using adaptive parameter selection. CoRR abs\/1210.4277."},{"key":"e_1_2_2_17_1","unstructured":"XU K. Stewart J. and Fiume E. 2002. Constraint-Based Automatic Placement for Scene Composition. In Graphics Interface 25--34.  XU K. Stewart J. and Fiume E. 2002. Constraint-Based Automatic Placement for Scene Composition. 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