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Graph."],"published-print":{"date-parts":[[2024,12,19]]},"abstract":"<jats:p>\n            This paper presents the first learning-based generative pipeline for effectively creating 3D LEGO\u00ae\n            <jats:sup>1<\/jats:sup>\n            models. This task is very challenging due to the lack of dedicated representations and datasets for learning coherently-connected bricks arrangements, as well as an immense design space that is combinatorial in nature. We approach this task by focusing on creating LEGO\u00ae micro buildings. Our contributions are four-fold. First, we propose the LEGO\u00ae semantic volume representation to encode LEGO\u00ae models, considering the bricks types and bricks connections, while allowing back-propagation learning. Second, we further consider the transformative nature of LEGO\n            <jats:sup>\u00ae<\/jats:sup>\n            to atomize the semantic volume and formulate a generative model to learn the representation. Third, we build a rich dataset of micro buildings for model learning. Last, we design the progressive reconstructor to create 3D LEGO\u00ae models from the generated representations, while ensuring bricks connections. We employed our pipeline to create LEGO\n            <jats:sup>\u00ae<\/jats:sup>\n            micro buildings with a wide array of bricks types, demonstrating its strong capability of learning diverse micro-building styles and producing assemble-able LEGO\u00ae models. Further, we performed various quantitative evaluations, ablations, and a user study to show the compelling capability of our approach in terms of generative quality, fidelity, and diversity.\n          <\/jats:p>","DOI":"10.1145\/3687755","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T15:46:04Z","timestamp":1732031164000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Learn to Create Simple LEGO Micro Buildings"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7318-2887","authenticated-orcid":false,"given":"Jiahao","family":"Ge","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8261-6949","authenticated-orcid":false,"given":"Mingjun","family":"Zhou","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5238-593X","authenticated-orcid":false,"given":"Chi-Wing","family":"Fu","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2024. 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