Publications

@inproceedings{upadhyay_cigma_2024,

title = {CIGMA: Automated 3D House Layout Generation through Generative Models},

author = {A. Upadhyay and A. Dubey and N. Bhardwaj and S. M. Kuriakose and R. Mohan},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183577885&doi=10.1145%2f3632410.3632490&partnerID=40&md5=cf0c249faf0ce03590010426e0f6c1e0},

doi = {10.1145/3632410.3632490},

isbn = {979-840071634-8 (ISBN)},

year  = {2024},

date = {2024-01-01},

booktitle = {ACM Int. Conf. Proc. Ser.},

pages = {542–546},

publisher = {Association for Computing Machinery},

abstract = {In this work, we introduce CIGMA, a metaverse platform that empowers designers to generate multiple house layout designs using generative models. We propose a generative adversarial network that synthesizes 2D layouts guided by user constraints. Our platform generates 3D views of house layouts and provides users with the ability to customize the 3D house model by generating furniture items and applying various textures for personalized floor and wall designs. We evaluate our approach on a large-scale dataset, RPLAN, consisting of 80,000 real floor plans from residential buildings. The qualitative and quantitative evaluations demonstrate the effectiveness of our approach over the existing baselines. The demo is accessible at https://youtu.be/lgb_V-yZ5lw. © 2024 Owner/Author.},

keywords = {3d house, 3D House Layout, 3D modeling, Floor Plan, Floorplans, Floors, Generative AI, Generative model, Houses, Large datasets, Layout designs, Layout generations, Metaverses, Textures, User constraints, Wall design},

pubstate = {published},

tppubtype = {inproceedings}

}