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We introduce a 3D object arrangement representation that models the locations and orientations of objects, based on their size and shape attributes. Moreover, our scene representation is applicable for 3D objects with different multiplicities (repetition counts), selected from a database. We show a principled way to train this model by combining discriminative losses for both a 3D object arrangement representation and a 2D image-based representation. We demonstrate the effectiveness of our scene representation and the network training method on benchmark datasets. We also show the applications of this generative model in scene interpolation and scene completion.<\/jats:p>","DOI":"10.1145\/3381866","type":"journal-article","created":{"date-parts":[[2020,4,9]],"date-time":"2020-04-09T09:42:07Z","timestamp":1586425327000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":64,"title":["Deep Generative Modeling for Scene Synthesis via Hybrid Representations"],"prefix":"10.1145","volume":"39","author":[{"given":"Zaiwei","family":"Zhang","sequence":"first","affiliation":[{"name":"The University of Texas at Austin, Austin, TX"}]},{"given":"Zhenpei","family":"Yang","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin, Austin, TX"}]},{"given":"Chongyang","family":"Ma","sequence":"additional","affiliation":[{"name":"Kuaishou Technology, China"}]},{"given":"Linjie","family":"Luo","sequence":"additional","affiliation":[{"name":"ByteDance, Inc."}]},{"given":"Alexander","family":"Huth","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin, Austin, TX"}]},{"given":"Etienne","family":"Vouga","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin, Austin, TX"}]},{"given":"Qixing","family":"Huang","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin, Austin, TX"}]}],"member":"320","published-online":{"date-parts":[[2020,4,9]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/882262.882311"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1073204.1073207"},{"key":"e_1_2_1_3_1","volume-title":"ICML (Proceedings of Machine Learning Research)","volume":"70","author":"Arjovsky Mart\u00edn","year":"2017","unstructured":"Mart\u00edn Arjovsky , Soumith Chintala , and L\u00e9on Bottou . 2017 . 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