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The emergence of the metaverse has led to the rapidly increasing demand for the generation of extensive 3D worlds. We consider that an engaging world is built upon a rational layout of multiple land-use areas (e.g., forest, meadow, and farmland). To this end, we propose a generative model of land-use distribution that learns from geographic data. The model is based on a transformer architecture that generates a 2D map of the land-use layout, which can be conditioned on spatial and semantic controls, depending on whether either one or both are provided. This model enables diverse layout generation with user control and layout expansion by extending borders with partial inputs. To generate high-quality and satisfactory layouts, we devise a geometric objective function that supervises the model to perceive layout shapes and regularize generations using geometric priors. Additionally, we devise a planning objective function that supervises the model to perceive progressive composition demands and suppress generations deviating from controls. To evaluate the spatial distribution of the generations, we train an autoencoder to embed land-use layouts into vectors to enable comparison between the real and generated data using the Wasserstein metric, which is inspired by the Fréchet inception distance. [ABSTRACT FROM AUTHOR]

Recent advanced Text-to-Image methods still require much work to specify all labels and detailed layouts of objects to obtain an accurate planned image. Layout-based synthesis is an alternative method enabling users to control detailed composition directly to avoid the trial-and-error often required in prompt-based editing. This paper proposes a layout generation from text instead of generating images directly. Our approach uses Transformer-based deep neural networks to synthesize scene representations of multiple objects. By focusing on layout information, we can make an explainable layout of what objects the image includes. Our end-to-end approach uses parallel decoding, differs from conventional layout synthesis, and introduces sequential object predictions and post-processing of duplicate bounding boxes. We experimentally compare our method’s quality and computational cost against existing ones, demonstrating its effectiveness and efficiency in generating layouts from textual representations. Combined with Layout-to-Image, this approach has significant practical implications, allowing the practical authoring tools that make image generation explainable and computable using relatively lightweight networks.

Layout generation is frequently encountered in the field of mechanical design. The direct application of generative adversarial network, which was originally used to generate pixel-level images, usually cannot guarantee the interrelation between components such as the non-overlap requirement. In addition, the number and the size of components cannot be precisely controlled. These all constitute the characteristics of mechanical layout. To address the above problems, we propose a hierarchical layout generation generative adversarial network (LGGAN) for mechanical layout generation. The layout generator consists of three modules. The first is hierarchical layout generation, where the shape and distribution of components are generated separately using two neural networks. Such a hierarchical structure greatly improves the generation capacity. To reduce the accumulated noise when multiple components are added, a denoiser is included as the second module. The third module is a refinement step used to fine-tune the layouts, which adjusts the size of each component to the prescribed value. All of the three modules are neural network-based, and can be trained through backpropagation. Additionally, an active learning strategy for training the LGGAN is proposed, which allows LGGAN to converge with a small amount of training data in situations where getting a significant amount of training data is not possible. Quantitative and qualitative experiments demonstrate the effectiveness of LGGAN. [ABSTRACT FROM AUTHOR]

This paper presents a design automation methodology for ring voltage-controlled oscillators (RVCOs) with their realistic and physical characteristics captured. With multiple sets of input constraints such as target frequency, phase noise, and control voltage range, the proposed algorithm automatically finds the design candidates that satisfy the target constraints, by running iterative post-layout simulations with auto-generated layouts and testbenches. The number of post-layout simulations is significantly reduced by the backtracking algorithm that observes the simulation results and determines the search direction. The proposed algorithm is applied to generate RVCOs in 40-nm planar and 7-nm FinFET technologies for DDR5 applications, and it turns out the proposed methodology produces sets of design parameters that meet the target specification in multiple technologies.

Generating architectural layouts from sites to flats, encompassing site layouts (SLs), building layouts (BLs), and flat layouts (FLs), presents a complex process. Notably, the BL generation is challenging due to the small scale of data, making it difficult to train effective neural networks. This paper introduces an approach for generating layouts throughout the complete process. Initially, it proposes an enhanced generative adversarial network (GAN) combined with the transformer for Stable Diffusion (TranSD-GAN), considering design boundaries and requirements. Subsequently, for generating BLs with small-scale datasets, the paper proposes a stacking transfer learning method. Following this, image operations are conducted to support the flow of building information. Ultimately, BIM models are created at each stage. Through comparative experiments involving neural networks and generation cases, it is demonstrated that the proposed method significantly improves the generative capabilities of small-scale datasets and effectively aids designers throughout the layout design from sites to flats. • Architectural layouts from sites to flats are generated automatically. • An enhanced GAN with the transformer from Stable Diffusion is proposed. • Stacking transfer learning is proposed for a small-scale building layout dataset. • Image operations are performed to streamline building information flow. [ABSTRACT FROM AUTHOR]

Automatic layout generation, which means making computers enjoy creativity, is difficult yet exciting work. Up to now, how to generate reasonable and visually appealing layouts remains a complex challenge. In this paper, we propose a novel layout generation model based on Conditional Generative Adversarial Networks (L-CGAN), which can generate layouts simply and efficiently by positioning, scaling, and flipping the given primitives. To break the bottleneck of limitation of the fixed input size of Generative Adversarial Networks, we develop a pre-processing algorithm to enable the model to generate layouts with an unrestricted number of input elements. Moreover, a graph-constraint module is proposed to guide layout optimization. We demonstrate the competitive performance of our designs in diverse data domains such as handwriting digit layout generation (MNIST Layouts), scene layout generation (AbstractScene-Layouts), and document layout generation (PubLayNet).

Layout generation is one of the earliest and most effective steps in designing a residential plan because the layout determines the quality and features of the final result. It is critical to consider the maximum match between each solution and the user-defined constraints, instant variations in constraints, comprehensible initial input, and the presentation of a series of solutions to each user. Thus, three objectives are pursued in this study: proposing a generative mechanism in which different user requirements and architectural design constraints play essential roles; recommending an explicit approach based on variables that are comprehensible to both architectures and users; automating some repetitive tasks related to the architectural presentation. This approach provides solutions that are as close to meeting user requirements as possible while keeping the architectural constraints. Accordingly, the final configuration is usable in the real world. [ABSTRACT FROM AUTHOR]

Purpose: The appropriate physical layout of hospital services can help resolve management problems by streamlining the work of medical teams, improving the flow of patients between specific areas and the medical support environment. Nevertheless, the academic literature lacks structured research into how the physical layout of hospitals might be improved. Our study aims to fill this research gap, providing information for researchers and professionals who intend to guide the hospital facility layout planning (HFLP) from the steps and prescribed approaches found in the literature., Design/methodology/approach: This study analyzes the current literature status and concerning approaches that support HFLP and identifies their strengths and weaknesses. The literature was classified using the following criteria: approaches for layout generation, approaches for layout evaluation and healthcare facility layout outcomes., Findings: The hospital facility layout outcomes achieved for each phase served as a basis for identifying a list of strengths and weaknesses for the hospital layout facility generation and evaluation approaches. Readers can refer to this paper to identify the approach that best fits the desired goal and the HFLP step., Practical Implications: This is a contribution to current studies into HFLP, and it provides guidelines for selecting the approach to be utilized based on the desired outcome., Originality/value: The paper describes how to conduct an HFLP and lists the strengths and weaknesses of each approach. The research may be used as a strategy for determining which tool is most suited based on the practitioner's target purpose., (© Emerald Publishing Limited.)

The use of the Digital Twin as a promising technology during the reconfiguration of automated systems enables to meet the challenges of increasing product diversity and shortening product life cycles in today's industry. The use of Digital Twins supports engineers in all phases of the life cycle of a production line. Over time, however, production facilities are often modified and improved, while at the same time the created models during the engineering process of the system remain unchanged and no longer correspond to the real facility. Manual updating of the positions in the digital layout is very time-consuming and therefore expensive. This paper presents a concept for the automatic update of the layout of a production line. In this concept, the positions of the robots and their active and passive peripheral devices are automatically positioned in a digital plant model using information from the current configuration of the robots in a production line. Therefore, engineers can use the resulting synchronized digital plant model, Digital Twin of the system, to further optimize or expand the production plant. [ABSTRACT FROM AUTHOR]

Layout generation, as a crucial stage in Artificial Intelligence-Generated Content (AIGC) task information presentation generation, is a fascinating and significant research topic. Automatically generating layouts intrigued the computer vision community dating back to the 1990s, and the advancement was sluggish for a time due to the inherent complexity of the issue. Thanks to the success of deep learning, layout generation is leading to a surge in both academic literature and business applications recently. Yet a systematic and comprehensive literature review of this domain is still missing. In this study, we provide a comprehensive overview of the state-of-the-art layout generation methods. Primarily, we discuss the problem of layout generation and associated background knowledge including layout representation, layout aesthetics, and generative models. Subsequently, recent advances in the field of intelligent layout generation are organized and summarized from three perspectives: innovations, models & algorithms, and applications. Furthermore, the paper outlines the datasets and evaluation methodology, both qualitative and quantitative, and briefly analyzes the selection of them for specific tasks. To sum up, we conclude the current challenges of intelligent layout generation and suggest possible directions for future endeavors. [Display omitted] In this study, we provide a comprehensive overview of the state-of-the-art layout generation methods. Primarily, we discuss the problem of layout generation and associated background knowledge including layout representation, layout aesthetics, and generative models. Subsequently, recent advances in the field of intelligent layout generation are organized and summarized from three perspectives: innovations, models & algorithms, and applications, as shown in Figure Fig. 1. Furthermore, the paper outlines the datasets and evaluation methodology, both qualitative and quantitative, and briefly analyzes the selection of them for specific tasks. To sum up, we conclude the current challenges of intelligent layout generation and suggest possible directions for future work. • Provide a thorough review of intelligent layout generation by investigating, categorizing, and summarizing recent advances based on deep learning, and fill in the missing work in this area. • Outline relevant datasets and evaluation methods for layout generation issues along with the specific application contexts. • Analyze and conclude current challenges in this domain and provide several possible directions in future work. [ABSTRACT FROM AUTHOR]

Graph visualisation is a well-known and researched field of graphical informatics. Several good algorithms were developed and reviewed by our days. However, most of the graph drawing tools mainly focus on static drawing generation. In this paper we present an approach that is efficient enough to visualise the user-requested parts (views) of a relatively large Semantic Program Graphs of Erlang projects in soft realtime. With the presented approach the visualised graphs can be traversed interactively, by changing between different levels of detailed information, which may support code comprehension in the RefactorErl framework. [ABSTRACT FROM AUTHOR]