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The network is trained on two sets of shapes, e.g., tables and chairs, while there is neither a pairing between shapes from the domains as supervision nor any point-wise correspondence between any shapes. Once trained, LOGAN takes a shape from one domain and transforms it into the other. Our network consists of an autoencoder to encode shapes from the two input domains into a\n            <jats:italic>common latent space<\/jats:italic>\n            , where the latent codes\n            <jats:italic>concatenate multi-scale<\/jats:italic>\n            shape features, resulting in an\n            <jats:italic>overcomplete<\/jats:italic>\n            representation. The translator is based on a generative adversarial network (GAN), operating in the latent space, where an adversarial loss enforces cross-domain translation while a\n            <jats:italic>feature preservation loss<\/jats:italic>\n            ensures that the right shape features are preserved for a natural shape transform. We conduct ablation studies to validate each of our key network designs and demonstrate superior capabilities in unpaired shape transforms on a variety of examples over baselines and state-of-the-art approaches. We show that LOGAN is able to learn what shape features to preserve during shape translation, either local or non-local, whether content or style, depending solely on the input domains for training.\n          <\/jats:p>","DOI":"10.1145\/3355089.3356494","type":"journal-article","created":{"date-parts":[[2019,11,8]],"date-time":"2019-11-08T20:27:58Z","timestamp":1573244878000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":36,"title":["LOGAN"],"prefix":"10.1145","volume":"38","author":[{"given":"Kangxue","family":"Yin","sequence":"first","affiliation":[{"name":"Simon Fraser University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqin","family":"Chen","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"}]},{"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[{"name":"Tel Aviv University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Simon Fraser University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2019,11,8]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Proc. of ICML.","author":"Achlioptas Panos","year":"2018","unstructured":"Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. 2018. 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