DeepScope: HCI Platform for Generative Cityscape Visualization

Noyman, Ariel; Larson, Kent

doi:10.1145/3334480.3382809

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…The DeepScope project ( Noyman and Larson, 2020 ) presents a real-time, generative platform for immersive urban-design visualization. In this work, the authors used a tangible user interface (TUI) to allow designers to iterate over urban designs quickly and observe generated images of the emergent street scene in real time ( Figure 7 : top left).…”

Section: Discussionmentioning

confidence: 99%

“…The reviewed articles’ largest outlet was conference proceedings (18), with 15 articles published in journals. One extended abstract ( Noyman and Larson, 2020 ) was included due to its scope and critical relevancy.…”

Section: Summary Of Literaturementioning

confidence: 99%

“…These reviews showcase the prevalence of GANs in research and indicate it as a growing area of importance. While there have been individual case studies into interactive systems that look at collaborative design with generative models ( Kato et al, 2019 ; Noyman and Larson, 2020 ), there has not been a systematic review looking at the area more broadly.…”

Section: Introductionmentioning

confidence: 99%

“…The emergence of artificial intelligence (AI) and machine learning (ML) as a crucial tool in the creative industries software toolbox has been staggering in scale. It is also one of the most active research areas in computer science (Murphy, 2012). Recent progress in generative deep learning (DL) techniques has led to a dearth of new solutions in the fields of computer graphics, vision, and user-aided design (Pan et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

Hughes

Zhu

Bednarz

2021

Front. Artif. Intell.

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The future of work and workplace is very much in flux. A vast amount has been written about artificial intelligence (AI) and its impact on work, with much of it focused on automation and its impact in terms of potential job losses. This review will address one area where AI is being added to creative and design practitioners’ toolbox to enhance their creativity, productivity, and design horizons. A designer’s primary purpose is to create, or generate, the most optimal artifact or prototype, given a set of constraints. We have seen AI encroaching into this space with the advent of generative networks and generative adversarial networks (GANs) in particular. This area has become one of the most active research fields in machine learning over the past number of years, and a number of these techniques, particularly those around plausible image generation, have garnered considerable media attention. We will look beyond automatic techniques and solutions and see how GANs are being incorporated into user pipelines for design practitioners. A systematic review of publications indexed on ScienceDirect, SpringerLink, Web of Science, Scopus, IEEExplore, and ACM DigitalLibrary was conducted from 2015 to 2020. Results are reported according to PRISMA statement. From 317 search results, 34 studies (including two snowball sampled) are reviewed, highlighting key trends in this area. The studies’ limitations are presented, particularly a lack of user studies and the prevalence of toy-examples or implementations that are unlikely to scale. Areas for future study are also identified.

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Section: Discussionmentioning

confidence: 99%

Section: Summary Of Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Generative Adversarial Networks–Enabled Human–Artificial Intelligence Collaborative Applications for Creative and Design Industries: A Systematic Review of Current Approaches and Trends

Hughes

Zhu

Bednarz

2021

Front. Artif. Intell.

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“…GANs and specifically DCGANs were applied in only a few studies to generate various urban plans and design schemes, including the street patterns (Hartmann et al, 2017; Owaki and Machida, 2020), architectural plan layout (Chaillou, 2020), street views (Noyman and Larson, 2020), and urbanization patterns (Albert et al, 2018). Some of them achieved great results.…”

Section: Introductionmentioning

confidence: 99%

Urban-GAN: An artificial intelligence-aided computation system for plural urban design

Quan

2022

Environment and Planning B: Urban Analytics and City Science

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The current urban design computation is mostly centered on the professional designer while ignoring the plural dimension of urban design. In addition, available public participation computational tools focus mainly on information and idea sharing, leaving the public excluded in design generation because of their lack of design expertise. To address such an issue, this study develops Urban-GAN, a plural urban design computation system, to provide new technical support for design empowerment, allowing the public to generate their own designs. The sub-symbolic representation and artificial intelligence techniques of deep convolutional neural networks, case-based reasoning, and generative adversarial networks are used to acquire and embody design knowledge as the density function, and generate design schemes with this knowledge. The system consists of an urban form database and five process models through which the user with little design expertise can select urban form cases, generate designs similar to those cases, and make design decisions. The Urban-GAN is applied to hypothetical design experiments, which show that the user is able to apply the system to successfully generate distinctive designs following the urban form “styles” in Manhattan, Portland, and Shanghai. This study further extends the discussion about the plural urban design computation to general reflections on the goals and values in AI technique application in planning and design.

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