Generating Topological Structure of Floorplans from Room Attributes

Yin, Yan; Hutchcroft, Will; Khosravan, Naji; Boyadzhiev, Ivaylo; Fu, Yun; Bing, Kang

doi:10.1145/3512527.3531384

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Yin et al. [YHK*22] propose the usage of graph attention networks for predicting the topological structure of floor plans using room attributes as graph nodes and room connectivity as graph edges. Para et al.…”

Section: Existing Procedural Modelling Methodsmentioning

confidence: 99%

A Survey of Procedural Modelling Methods for Layout Generation of Virtual Scenes

Cogo,

Krupalija,

Prazina

et al. 2023

Computer Graphics Forum

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As virtual worlds continue to rise in popularity, so do the expectations of users for the content of virtual scenes. Virtual worlds must be large in scope and offer enough freedom of movement to keep the audience occupied at all times. For content creators, it is difficult to keep up by manually producing the surrounding content. Therefore, the application of procedural modelling techniques is required. Virtual worlds often mimic the real world, which is composed of organized and connected outdoor and indoor layouts. It is expected that all content is present on the virtual scene and that a user can navigate streets, enter buildings, and interact with furniture within a single virtual world. While there are many procedural methods for generating different layout types, they mostly focus only on one layout type, whereas complete scene generation is greatly underrepresented. This paper aims to identify the coverage of layout types by different methods because similar issues exist for the generation of content of different layout types. When creating a new method for layout generation, it is important to know if the results of existing methods can be appended to other methods. This paper presents a survey of existing procedural modelling methods, which were organized into five categories based on the core approach: pure subdivision, grammar‐based, data‐driven, optimization, and simulation. Information about the covered layout types, the possibility of user interaction during the generation process, and the input and output shape types of the generated content is provided for each surveyed method. The input and output shape types of the generated content can be useful to identify which methods can continue the generation by using the output of other methods as their input. It was concluded that all surveyed methods work for only a few different layout types simultaneously. Moreover, only 35% of the surveyed methods offer interaction with the user after completing the initial process of space generation. Most existing approaches do not perform transformations of shape types. A significant number of methods use the irregular shape type as input and generate the same shape type as the output, which is sufficient for coverage of all layout types when generating a complete virtual world.

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Section: Existing Procedural Modelling Methodsmentioning

confidence: 99%

A Survey of Procedural Modelling Methods for Layout Generation of Virtual Scenes

Cogo,

Krupalija,

Prazina

et al. 2023

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…Generative Adversarial Nets (GANs) [264,265,266,267,268,269] is one of the major frameworks in image generation. [270] attempt to address the facial generation.…”

Section: Related Workmentioning

confidence: 99%

Unveiling the power of transfer learning towards efficient artificial intelligence

Qin

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Hybrid multilingual generation in resolution of 512 × 512. There are three-differentlanguage texts in the input caption including Chinese, Japanese and English. The caption of (a) is "colorful, a cat painted by Picasso, sit on a table, is eating food" and the caption of (b) is "a white, sedan, crash into a building". With our GlueNet infused ahead, the XLM-Roberta can guide SDM decoder to generate reasonable results where the original SDM fails to work.

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“…In today's technology landscape, cutting-edge products and advancements rely heavily on large models [10,11,12], demanding substantial datasets for effective training. The accuracy and reliability of these models are intricately tied to the scale and quality of the training data [13,14,15]. For example, ChatGPT [16], developed by OpenAI in 2020, leverages extensive textual data to craft human-like responses, while the evolution of autonomous driving, pioneered by Google and embraced by companies like Tesla and Waymo, hinges on intricate sensors and machine learning algorithms that analyze and act upon data.…”

Section: Introductionmentioning

confidence: 99%

Synthetic data generator: understanding human face & body via image synthesis

Yin

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Besides, I wish to thank all members of the SMILELab for their assistance, guidance, and of course friendship during my PH.D. degree.Last but not least, I would like to express my deepest gratitude to my family for providing me with continuous support throughout my years of study and research. And I would also like to thank my best friend S. Zhu for always being understanding and making everything brighter.

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Generating Topological Structure of Floorplans from Room Attributes

Cited by 6 publications

References 28 publications

A Survey of Procedural Modelling Methods for Layout Generation of Virtual Scenes

A Survey of Procedural Modelling Methods for Layout Generation of Virtual Scenes

Unveiling the power of transfer learning towards efficient artificial intelligence

Synthetic data generator: understanding human face & body via image synthesis

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