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    FrankenGAN: Guided detail synthesis for building mass models using style-Synchonized Gans

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    1806.07179.pdf
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    Description:
    Accepted Manuscript
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    Type
    Article
    Authors
    Kelly, Tom
    Guerrero, Paul
    Steed, Anthony
    Wonka, Peter cc
    Mitra, Niloy J.
    KAUST Department
    Computer Science Program
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    KAUST Grant Number
    OSR-2015-CCF-2533
    OSR-CRG2017-3426
    Date
    2018-11-01
    Online Publication Date
    2018-11-28
    Print Publication Date
    2018-12-04
    Permanent link to this record
    http://hdl.handle.net/10754/656365
    
    Metadata
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    Abstract
    Coarse building mass models are now routinely generated at scales ranging from individual buildings to whole cities. Such models can be abstracted from raw measurements, generated procedurally, or created manually. However, these models typically lack any meaningful geometric or texture details, making them unsuitable for direct display. We introduce the problem of automatically and realistically decorating such models by adding semantically consistent geometric details and textures. Building on the recent success of generative adversarial networks (GANs), we propose FrankenGAN, a cascade of GANs that creates plausible details across multiple scales over large neighborhoods. The various GANs are synchronized to produce consistent style distributions over buildings and neighborhoods.We provide the user with direct control over the variability of the output. We allow him/her to interactively specify the style via images and manipulate style-adapted sliders to control style variability. We test our system on several large-scale examples. The generated outputs are qualitatively evaluated via a set of perceptual studies and are found to be realistic, semantically plausible, and consistent in style.
    Citation
    Kelly, T., Guerrero, P., Steed, A., Wonka, P., & Mitra, N. J. (2018). FrankenGAN. ACM Transactions on Graphics, 37(6), 1–14. doi:10.1145/3272127.3275065
    Sponsors
    This project was supported by an ERC Starting Grant (SmartGeometry StG-2013-335373), KAUST-UCL Grant (OSR-2015-CCF-2533), ERC PoC Grant (SemanticCity), the KAUST Office of Sponsored Research (OSR-CRG2017-3426), Open3D Project (EPSRC Grant EP/M013685/1), and a Google Faculty Award (UrbanPlan).
    Publisher
    Association for Computing Machinery acmhelp@acm.org
    Journal
    ACM Transactions on Graphics
    DOI
    10.1145/3272127.3275065
    arXiv
    1806.07179
    Additional Links
    http://dl.acm.org/citation.cfm?doid=3272127.3275065
    http://arxiv.org/pdf/1806.07179
    ae974a485f413a2113503eed53cd6c53
    10.1145/3272127.3275065
    Scopus Count
    Collections
    Articles; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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