Jiachuan Shen’s research while affiliated with University College London and other places

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Publications (2)


A Survey on Deep Learning for Design and Generation of Virtual Architecture
  • Article

September 2024

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79 Reads

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4 Citations

ACM Computing Surveys

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3D shape generation techniques leveraging deep learning have garnered significant interest from both computer vision and architectural design communities, promising to enrich the content in the virtual environment. However, research on virtual architectural design remains limited, particularly regarding designer-AI collaboration and deep learning-assisted design. In our survey, we reviewed 149 related articles (81.2% of articles published between 2019 and 2023) covering architectural design, 3D shape techniques, and virtual environments. Through scrutinizing the literature, we first identify the principles of virtual architecture and illuminate its current production challenges, including datasets, multimodality, design intuition, and generative frameworks. We then introduce the latest approaches to designing and generating virtual buildings leveraging 3D shape generation and summarize four characteristics of various approaches to virtual architecture. Based on our analysis, we expound on four research agendas, including agency, communication, user consideration, and integrating tools. Additionally, we highlight four important enablers of ubiquitous interaction with immersive systems in deep learning-assisted architectural generation. Our work contributes to fostering understanding between designers and deep learning techniques, broadening access to designer-AI collaboration. We advocate for interdisciplinary efforts to address this timely research topic, facilitating content designing and generation in the virtual environment.


Towards Computational Architecture of Liberty: A Comprehensive Survey on Deep Learning for Generating Virtual Architecture in the Metaverse
  • Preprint
  • File available

April 2023

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599 Reads

3D shape generation techniques utilizing deep learning are increasing attention from both computer vision and architectural design. This survey focuses on investigating and comparing the current latest approaches to 3D object generation with deep generative models (DGMs), including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), 3D-aware images, and diffusion models. We discuss 187 articles (80.7% of articles published between 2018-2022) to review the field of generated possibilities of architecture in virtual environments, limited to the architecture form. We provide an overview of architectural research, virtual environment, and related technical approaches, followed by a review of recent trends in discrete voxel generation, 3D models generated from 2D images, and conditional parameters. We highlight under-explored issues in 3D generation and parameterized control that is worth further investigation. Moreover, we speculate that four research agendas including data limitation, editability, evaluation metrics, and human-computer interaction are important enablers of ubiquitous interaction with immersive systems in architecture for computer-aided design Our work contributes to researchers' understanding of the current potential and future needs of deep learnings in generating virtual architecture.

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Citations (1)


... Challenges related to integrating advanced retrieval-augmented generation techniques were identified for future research. Wang et al. [130] designed an LLM agent framework for personal mobility generation. This framework incorporates real-world urban mobility data, aligning activity generation with individual patterns and motivations through retrieval-augmented strategies. ...

Reference:

Generative Spatial Artificial Intelligence for Sustainable Smart Cities: A Pioneering Large Flow Model for Urban Digital Twin
A Survey on Deep Learning for Design and Generation of Virtual Architecture
  • Citing Article
  • September 2024

ACM Computing Surveys