{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:30:23Z","timestamp":1784997023722,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":40,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"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":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3680681","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:27Z","timestamp":1729925967000},"page":"3248-3256","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Cons2Plan: Vector Floorplan Generation from Various Conditions via a Learning Framework based on Conditional Diffusion Models"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6403-166X","authenticated-orcid":false,"given":"Shibo","family":"Hong","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8571-9780","authenticated-orcid":false,"given":"Xuhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0896-0690","authenticated-orcid":false,"given":"Tianyu","family":"Du","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4919-7924","authenticated-orcid":false,"given":"Sheng","family":"Cheng","sequence":"additional","affiliation":[{"name":"Shenzhou Aerospace Software Technology Company Limited, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7364-8461","authenticated-orcid":false,"given":"Xun","family":"Wang","sequence":"additional","affiliation":[{"name":"Shenzhou Aerospace Software Technology Company Limited, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4703-7348","authenticated-orcid":false,"given":"Jianwei","family":"Yin","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5220\/0005209202710278"},{"key":"e_1_3_2_1_2_1","unstructured":"Jacob Austin Daniel D. Johnson Jonathan Ho Daniel Tarlow and Rianne van den Berg. 2023. Structured Denoising Diffusion Models in Discrete State-Spaces. arxiv: 2107.03006 [cs]"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2209.02646"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978--981--15--6568--7_8"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01264"},{"key":"e_1_3_2_1_6_1","volume-title":"Analog Bits: Generating Discrete Data Using Diffusion Models with Self-Conditioning. arxiv: 2208.04202 [cs]","author":"Chen Ting","year":"2023","unstructured":"Ting Chen, Ruixiang Zhang, and Geoffrey Hinton. 2023. Analog Bits: Generating Discrete Data Using Diffusion Models with Self-Conditioning. arxiv: 2208.04202 [cs]"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/WoWMoM.2011.5986141"},{"key":"e_1_3_2_1_8_1","volume-title":"ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models","author":"Cao Nicola De","year":"2018","unstructured":"Nicola De Cao and Thomas Kipf. 2018. MolGAN: An implicit generative model for small molecular graphs. ICML 2018 workshop on Theoretical Foundations and Applications of Deep Generative Models (2018)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01977"},{"key":"e_1_3_2_1_10_1","unstructured":"Prafulla Dhariwal and Alex Nichol. 2021. Diffusion Models Beat GANs on Image Synthesis. arxiv: 2105.05233 [cs stat]"},{"key":"e_1_3_2_1_11_1","volume-title":"A Generalization of Transformer Networks to Graphs. CoRR","author":"Dwivedi Vijay Prakash","year":"2020","unstructured":"Vijay Prakash Dwivedi and Xavier Bresson. 2020. A Generalization of Transformer Networks to Graphs. CoRR, Vol. abs\/2012.09699 (2020). https:\/\/arxiv.org\/abs\/2012.09699"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_13_1","volume-title":"Advances in Neural Information Processing Systems","author":"Heusel Martin","year":"2017","unstructured":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/8a1d694707eb0fefe65871369074926d-Paper.pdf"},{"key":"e_1_3_2_1_14_1","volume-title":"Classifier-Free Diffusion Guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications. https:\/\/openreview.net\/forum?id=qw8AKxfYbI","author":"Ho Jonathan","year":"2021","unstructured":"Jonathan Ho and Tim Salimans. 2021. Classifier-Free Diffusion Guidance. In NeurIPS 2021 Workshop on Deep Generative Models and Downstream Applications. https:\/\/openreview.net\/forum?id=qw8AKxfYbI"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3386569.3392391"},{"key":"e_1_3_2_1_16_1","volume-title":"Perceiver IO: A General Architecture for Structured Inputs & Outputs. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=fILj7WpI-g","author":"Jaegle Andrew","year":"2022","unstructured":"Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier J Henaff, Matthew Botvinick, Andrew Zisserman, Oriol Vinyals, and Joao Carreira. 2022. Perceiver IO: A General Architecture for Structured Inputs & Outputs. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=fILj7WpI-g"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning. 4651--4664","author":"Jaegle Andrew","year":"2021","unstructured":"Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira. 2021. Perceiver: General Perception with Iterative Attention. In Proceedings of the 38th International Conference on Machine Learning. 4651--4664. https:\/\/proceedings.mlr.press\/v139\/jaegle21a.html"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00014"},{"key":"e_1_3_2_1_19_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2017","unstructured":"Diederik P. Kingma and Jimmy Ba. 2017. Adam: A Method for Stochastic Optimization. arxiv: 1412.6980 [cs]"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.21437\/Interspeech.2021--36"},{"key":"e_1_3_2_1_21_1","volume-title":"Hashimoto","author":"Li Xiang Lisa","year":"2022","unstructured":"Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B. Hashimoto. 2022. Diffusion-LM Improves Controllable Text Generation. arxiv: 2205.14217 [cs]"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00037"},{"key":"e_1_3_2_1_23_1","volume-title":"Decoupled Weight Decay Regularization. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Bkg6RiCqY7","author":"Loshchilov Ilya","year":"2019","unstructured":"Ilya Loshchilov and Frank Hutter. 2019. Decoupled Weight Decay Regularization. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Bkg6RiCqY7"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1882262.1866203"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978--3-030--58452--8_10"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01342"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/AAAI.V32I1.11671"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00529"},{"key":"e_1_3_2_1_30_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=S1esMkHYPr","author":"Chence","year":"2020","unstructured":"Chence Shi*, Minkai Xu*, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang. 2020. GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=S1esMkHYPr"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356529"},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning. 6105--6114","author":"Tan Mingxing","year":"2019","unstructured":"Mingxing Tan and Quoc Le. 2019. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proceedings of the 36th International Conference on Machine Learning. 6105--6114. https:\/\/proceedings.mlr.press\/v97\/tan19a.html"},{"key":"e_1_3_2_1_33_1","volume-title":"\u0141 ukasz Kaiser, and Illia Polosukhin","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141 ukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems, I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.), Vol. 30. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf"},{"key":"e_1_3_2_1_34_1","volume-title":"The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=UaAD-Nu86WX","author":"Vignac Clement","year":"2023","unstructured":"Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard. 2023. DiGress: Discrete Denoising diffusion for graph generation. In The Eleventh International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=UaAD-Nu86WX"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.14357"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3355089.3356556"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626235"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning. 5708--5717","author":"You Jiaxuan","year":"2018","unstructured":"Jiaxuan You, Rex Ying, Xiang Ren, William Hamilton, and Jure Leskovec. 2018. GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models. In Proceedings of the 35th International Conference on Machine Learning. 5708--5717. https:\/\/proceedings.mlr.press\/v80\/you18a.html"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403104"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2952452"}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","location":"Melbourne VIC Australia","acronym":"MM '24","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680681","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3680681","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:57Z","timestamp":1750295877000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680681"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":40,"alternative-id":["10.1145\/3664647.3680681","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3680681","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}