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Generating realistic building layouts for automatic building design has been studied in both the computer vision and architecture domains. Traditional approaches from the architecture domain, which are based on optimization techniques or…

计算机视觉与模式识别 · 计算机科学 2025-04-15 Jiachen Liu , Yuan Xue , Haomiao Ni , Rui Yu , Zihan Zhou , Sharon X. Huang

Optimizing warehouse layouts is crucial due to its significant impact on efficiency and productivity. We present an AI-driven framework for automated warehouse layout generation. This framework employs constrained beam search to derive…

Developing fully parametric building models for performance-based generative design tasks often requires proficiency in many advanced 3D modeling and visual programming, limiting its use for many building designers. Moreover, iterations of…

We propose a new generative model for layout generation. We generate layouts in three steps. First, we generate the layout elements as nodes in a layout graph. Second, we compute constraints between layout elements as edges in the layout…

计算机视觉与模式识别 · 计算机科学 2020-11-30 Wamiq Para , Paul Guerrero , Tom Kelly , Leonidas Guibas , Peter Wonka

The essential task of urban planning is to generate the optimal land-use configuration of a target area. However, traditional urban planning is time-consuming and labor-intensive. Deep generative learning gives us hope that we can automate…

人工智能 · 计算机科学 2022-12-05 Dongjie Wang , Lingfei Wu , Denghui Zhang , Jingbo Zhou , Leilei Sun , Yanjie Fu

Recent studies show increasing demands and interests in automatically generating layouts, while there is still much room for improving the plausibility and robustness. In this paper, we present a data-driven layout framework without model…

图形学 · 计算机科学 2021-01-11 Shao-Kui Zhang , Wei-Yu Xie , Song-Hai Zhang

It is common in graphic design humans visually arrange various elements according to their design intent and semantics. For example, a title text almost always appears on top of other elements in a document. In this work, we generate…

计算机视觉与模式识别 · 计算机科学 2021-08-03 Kotaro Kikuchi , Edgar Simo-Serra , Mayu Otani , Kota Yamaguchi

Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work…

机器学习 · 计算机科学 2020-05-27 Sangeun Oh , Yongsu Jung , Seongsin Kim , Ikjin Lee , Namwoo Kang

Efficient authoring of vast virtual environments hinges on algorithms that are able to automatically generate content while also being controllable. We propose a method to automatically generate furniture layouts for indoor environments.…

计算机视觉与模式识别 · 计算机科学 2019-01-28 Paul Henderson , Kartic Subr , Vittorio Ferrari

Urban modeling is essential for city planning, scene synthesis, and gaming. Existing image-based methods generate diverse layouts but often lack geometric continuity and scalability, while graph-based methods capture structural relations…

计算机视觉与模式识别 · 计算机科学 2025-09-30 Mengyuan Niu , Xinxin Zhuo , Ruizhe Wang , Yuyue Huang , Junyan Yang , Qiao Wang

We are dealing with the problem of space layout planning here. We present an architectural conceptual CAD approach. Starting with design specifications in terms of constraints over spaces, a specific enumeration heuristics leads to a…

人工智能 · 计算机科学 2013-03-19 Benachir Medjdoub , Bernard Yannou

This paper presents a novel paradigm in simulation-based engineering sciences by introducing a new framework called Generative Parametric Design (GPD). The GPD framework enables the generation of new designs along with their corresponding…

计算工程、金融与科学 · 计算机科学 2025-12-15 Mohammed El Fallaki Idrissi , Jad Mounayer , Sebastian Rodriguez , Fodil Meraghni , Francisco Chinesta

In recent years generative design techniques have become firmly established in numerous applied fields, especially in engineering. These methods are demonstrating intensive growth owing to promising outlook. However, existing approaches are…

Layout designs are encountered in a variety of fields. For problems with many design degrees of freedom, efficiency of design methods becomes a major concern. In recent years, machine learning methods such as artificial neural networks have…

机器学习 · 计算机科学 2021-02-01 Chao Qian , Renkai Tan , Wenjing Ye

Automated floorplanning or space layout planning has been a long-standing NP-hard problem in the field of computer-aided design, with applications in integrated circuits, architecture, urbanism, and operational research. In this paper, we…

图形学 · 计算机科学 2021-02-23 Mohammad Keshavarzi , Mohammad Rahmani-Asl

Modeling layout is an important first step for graphic design. Recently, methods for generating graphic layouts have progressed, particularly with Generative Adversarial Networks (GANs). However, the problem of specifying the locations and…

计算机视觉与模式识别 · 计算机科学 2020-09-14 Jianan Li , Jimei Yang , Jianming Zhang , Chang Liu , Christina Wang , Tingfa Xu

Generative systems are becoming a crucial part of current design practice. There exist gaps however, between the digital processes, field data and designer's input. To solve this problem, multiple processes were developed in order to…

图形学 · 计算机科学 2023-12-22 Demircan Tas , Osman Sumer

Recent advances in generative modeling, namely Diffusion models, have revolutionized generative modeling, enabling high-quality image generation tailored to user needs. This paper proposes a framework for the generative design of structural…

Current performance-driven building design methods are not widely adopted outside the research field for several reasons that make them difficult to integrate into a typical design process. In the early design phase, in particular, the…

机器学习 · 计算机科学 2022-04-19 Spyridon Ampanavos , Ali Malkawi

The recent surge in interest in city layout generation underscores its significance in urban planning and smart city development. The task involves procedurally or automatically generating spatial arrangements for urban elements such as…

计算机视觉与模式识别 · 计算机科学 2025-04-14 Jie Deng , Wenhao Chai , Jianshu Guo , Qixuan Huang , Junsheng Huang , Wenhao Hu , Shengyu Hao , Jenq-Neng Hwang , Gaoang Wang
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