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Exploring Text-based Realistic Building Facades Editing Applicaiton

Jing Wang, Xin Zhang

TL;DR

This work addresses the need for localized, semantically guided facade edits in architecture using diffusion-based image editing. It proposes Blended Latent Diffusion, a text-guided editing framework that operates in latent spaces to enable precise, local modifications while preserving realism, with optional background optimization and CLIP-based result ranking. The paper demonstrates rapid, mask-guided facade edits and discusses the trade-offs, including feature representation challenges and computational requirements, supported by a visual feasibility analysis. The approach offers a practical workflow for architects and urban planners to iteratively explore design variations with semantic control and personalized customization, potentially accelerating façade design workflows.

Abstract

This paper explores the utilization of diffusion models and textual guidance for achieving localized editing of building facades, addressing the escalating demand for sophisticated editing methodologies in architectural design and urban planning. Leveraging the robust generative capabilities of diffusion models, this study presents a promising avenue for realistically synthesizing and modifying architectural facades. Through iterative diffusion and text descriptions, these models adeptly capture both the intricate global and local structures inherent in architectural facades, thus effectively navigating the complexity of such designs. Additionally, the paper examines the expansive potential of diffusion models in various facets, including the generation of novel facade designs, the enhancement of existing facades, and the realization of personalized customization. Despite their promise, diffusion models encounter obstacles such as computational resource constraints and data imbalances. To address these challenges, the study introduces the innovative Blended Latent Diffusion method for architectural facade editing, accompanied by a comprehensive visual analysis of its viability and efficacy. Through these endeavors, we aims to propel forward the field of architectural facade editing, contributing to its advancement and practical application.

Exploring Text-based Realistic Building Facades Editing Applicaiton

TL;DR

This work addresses the need for localized, semantically guided facade edits in architecture using diffusion-based image editing. It proposes Blended Latent Diffusion, a text-guided editing framework that operates in latent spaces to enable precise, local modifications while preserving realism, with optional background optimization and CLIP-based result ranking. The paper demonstrates rapid, mask-guided facade edits and discusses the trade-offs, including feature representation challenges and computational requirements, supported by a visual feasibility analysis. The approach offers a practical workflow for architects and urban planners to iteratively explore design variations with semantic control and personalized customization, potentially accelerating façade design workflows.

Abstract

This paper explores the utilization of diffusion models and textual guidance for achieving localized editing of building facades, addressing the escalating demand for sophisticated editing methodologies in architectural design and urban planning. Leveraging the robust generative capabilities of diffusion models, this study presents a promising avenue for realistically synthesizing and modifying architectural facades. Through iterative diffusion and text descriptions, these models adeptly capture both the intricate global and local structures inherent in architectural facades, thus effectively navigating the complexity of such designs. Additionally, the paper examines the expansive potential of diffusion models in various facets, including the generation of novel facade designs, the enhancement of existing facades, and the realization of personalized customization. Despite their promise, diffusion models encounter obstacles such as computational resource constraints and data imbalances. To address these challenges, the study introduces the innovative Blended Latent Diffusion method for architectural facade editing, accompanied by a comprehensive visual analysis of its viability and efficacy. Through these endeavors, we aims to propel forward the field of architectural facade editing, contributing to its advancement and practical application.
Paper Structure (9 sections, 2 figures)

This paper contains 9 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Related Method
  3. Local Editing Scenario
  4. Diffusion Models
  5. Text Guidance
  6. Background Optimization
  7. Ranking Effects
  8. Facade Editing
  9. Conclusion

Figures (2)

  • Figure 1: We can control the editing areas and achieve local modifications of building facades via user-drawn masks and prompts
  • Figure 2: Diffusion models possess powerful generative capabilities, allowing them to realistically synthesize details and structures of architectural facades. By combining with user-drawn masks, we can precisely control the editing areas and achieve local modifications of building appearances. This method not only enables rapid generation of edited images but also preserves the realism and quality of the images.