Insights Informed Generative AI for Design: Incorporating Real-world Data for Text-to-Image Output

• URL: http://arxiv.org/abs/2506.15008v1
• Date: Tue, 17 Jun 2025 22:33:11 GMT
• Title: Insights Informed Generative AI for Design: Incorporating Real-world Data for Text-to-Image Output
• Authors: Richa Gupta, Alexander Htet Kyaw,
• Abstract summary: We propose a novel pipeline that integrates DALL-E 3 with a materials dataset to enrich AI-generated designs with sustainability metrics and material usage insights.<n>We evaluate the system through three user tests: (1) no mention of sustainability to the user prior to the prompting process with generative AI, (2) sustainability goals communicated to the user before prompting, and (3) sustainability goals communicated along with quantitative CO2e data included in the generative AI outputs.
• Score: 51.88841610098437
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Generative AI, specifically text-to-image models, have revolutionized interior architectural design by enabling the rapid translation of conceptual ideas into visual representations from simple text prompts. While generative AI can produce visually appealing images they often lack actionable data for designers In this work, we propose a novel pipeline that integrates DALL-E 3 with a materials dataset to enrich AI-generated designs with sustainability metrics and material usage insights. After the model generates an interior design image, a post-processing module identifies the top ten materials present and pairs them with carbon dioxide equivalent (CO2e) values from a general materials dictionary. This approach allows designers to immediately evaluate environmental impacts and refine prompts accordingly. We evaluate the system through three user tests: (1) no mention of sustainability to the user prior to the prompting process with generative AI, (2) sustainability goals communicated to the user before prompting, and (3) sustainability goals communicated along with quantitative CO2e data included in the generative AI outputs. Our qualitative and quantitative analyses reveal that the introduction of sustainability metrics in the third test leads to more informed design decisions, however, it can also trigger decision fatigue and lower overall satisfaction. Nevertheless, the majority of participants reported incorporating sustainability principles into their workflows in the third test, underscoring the potential of integrated metrics to guide more ecologically responsible practices. Our findings showcase the importance of balancing design freedom with practical constraints, offering a clear path toward holistic, data-driven solutions in AI-assisted architectural design.

Related papers

• Towards Agentic Intelligence for Materials Science [73.4576385477731]
  This survey advances a unique pipeline-centric view that spans from corpus curation and pretraining to goal-conditioned agents interfacing with simulation and experimental platforms.<n>To bridge communities and establish a shared frame of reference, we first present an integrated lens that aligns terminology, evaluation, and workflow stages across AI and materials science.
  arXiv  Detail & Related papers  (2026-01-29T23:48:43Z)
• Sustainable Materials Discovery in the Era of Artificial Intelligence [3.222363676081407]
  We propose to integrate upstream machine learning (ML) assisted materials discovery with downstream lifecycle assessment into a uniform ML-LCA environment.<n>The framework ML-LCA integrates five components, information extraction for building materials-environment knowledge bases, harmonized databases linking properties to sustainability metrics, multi-scale models bridging atomic properties to lifecycle impacts, ensemble prediction of manufacturing pathways with uncertainty quantification, and uncertainty-aware optimization.
  arXiv  Detail & Related papers  (2026-01-29T10:42:44Z)
• Bridging Cognitive Gap: Hierarchical Description Learning for Artistic Image Aesthetics Assessment [51.40989269202702]
  aesthetic quality assessment task is crucial for developing a human-aligned quantitative evaluation system for AIGC.<n>We propose ArtQuant, an aesthetics assessment framework for artistic images which couples isolated aesthetic dimensions through description generation.<n>Our approach achieves epoch state-of-the-art performance on several datasets while requiring only 33% of conventional trainings.
  arXiv  Detail & Related papers  (2025-12-29T12:18:26Z)
• Q-REAL: Towards Realism and Plausibility Evaluation for AI-Generated Content [71.46991494014382]
  We introduce Q-Real, a novel dataset for fine-grained evaluation of realism and plausibility in AI-generated images.<n>Q-Real consists of 3,088 images generated by popular text-to-image models.<n>We construct Q-Real Bench to evaluate them on two tasks: judgment and grounding with reasoning.
  arXiv  Detail & Related papers  (2025-11-21T02:43:17Z)
• Attention is also needed for form design [0.0]
  This research introduces a novel, attention-aware framework that integrates two systems: EURIA, an immersive Virtual Reality environment using eye-tracking, and RETina, an agentic AI pipeline.<n>The proposed framework elevates the designer's role to that of a creative director, synergizing human intuition with the generative power of agentic AI to produce higher-quality designs more efficiently.
  arXiv  Detail & Related papers  (2025-08-27T09:15:21Z)
• Large Language Models as Visualization Agents for Immersive Binary Reverse Engineering [0.0]
  Immersive virtual reality (VR) offers affordances that may reduce cognitive complexity in binary reverse engineering (RE)<n>We extend that platform with an integrated large language model (LLM) agent capable of querying binary analysis tools and generating immersive 3D visualizations.
  arXiv  Detail & Related papers  (2025-08-19T00:24:01Z)
• Multi-Agent Synergy-Driven Iterative Visual Narrative Synthesis [2.846897538377738]
  We introduce RCPS, a novel framework for automated generation of high-quality media presentations.<n>We also propose PREVAL, a preference-based evaluation framework to assess presentation quality across Content, Coherence, and Design.<n>PREVAL shows strong correlation with human judgments, validating it as a reliable automated tool for assessing presentation quality.
  arXiv  Detail & Related papers  (2025-07-17T16:50:07Z)
• AI-powered Contextual 3D Environment Generation: A Systematic Review [49.1574468325115]
  This study performs a systematic review of existing generative AI techniques for 3D scene generation.<n>By examining state-of-the-art approaches, it presents key challenges such as scene authenticity and the influence of textual inputs.
  arXiv  Detail & Related papers  (2025-06-05T15:56:28Z)
• Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy [0.0]
  We argue for a new generation of design systems that transcend black-box modeling by integrating domain knowledge, physics-informed learning, and intuitive human-AI interfaces.<n>Through illustrative examples and conceptual frameworks, we advocate that inverse design in manufacturing should evolve into a unified ecosystem.
  arXiv  Detail & Related papers  (2025-05-29T08:15:27Z)
• PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides [51.88536367177796]
  We propose a two-stage, edit-based approach inspired by human drafts for automatically generating presentations.<n>PWTAgent first analyzes references to extract slide-level functional types and content schemas, then generates editing actions based on selected reference slides.<n>PWTAgent significantly outperforms existing automatic presentation generation methods across all three dimensions.
  arXiv  Detail & Related papers  (2025-01-07T16:53:01Z)
• Data Analysis in the Era of Generative AI [56.44807642944589]
  This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps.
  arXiv  Detail & Related papers  (2024-09-27T06:31:03Z)
• Ethical-Lens: Curbing Malicious Usages of Open-Source Text-to-Image Models [51.69735366140249]
  We introduce Ethical-Lens, a framework designed to facilitate the value-aligned usage of text-to-image tools.<n>Ethical-Lens ensures value alignment in text-to-image models across toxicity and bias dimensions.<n>Our experiments reveal that Ethical-Lens enhances alignment capabilities to levels comparable with or superior to commercial models.
  arXiv  Detail & Related papers  (2024-04-18T11:38:25Z)
• Generative AI in the Construction Industry: A State-of-the-art Analysis [0.4241054493737716]
  There is a gap in the literature on the current state, opportunities, and challenges of generative AI in the construction industry. This study aims to review and categorize the existing and emerging generative AI opportunities and challenges in the construction industry. It proposes a framework for construction firms to build customized generative AI solutions using their own data.
  arXiv  Detail & Related papers  (2024-02-15T13:39:55Z)
• Clairvoyance: A Pipeline Toolkit for Medical Time Series [95.22483029602921]
  Time-series learning is the bread and butter of data-driven *clinical decision support* Clairvoyance proposes a unified, end-to-end, autoML-friendly pipeline that serves as a software toolkit. Clairvoyance is the first to demonstrate viability of a comprehensive and automatable pipeline for clinical time-series ML.
  arXiv  Detail & Related papers  (2023-10-28T12:08:03Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)
• Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions [0.0]
  We review advances in AI-driven materials-by-design and their applications to energetic materials. We evaluate methods in the literature in terms of their capacity to learn from a small/limited number of data. We suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning.
  arXiv  Detail & Related papers  (2022-11-15T14:41:11Z)
• Material Prediction for Design Automation Using Graph Representation Learning [5.181429907321226]
  We introduce a graph representation learning framework that supports the material prediction of bodies in assemblies. We formulate the material selection task as a node-level prediction task over the assembly graph representation of CAD models and tackle it using Graph Neural Networks (GNNs) The proposed framework can scale to large datasets and incorporate designers' knowledge into the learning process.
  arXiv  Detail & Related papers  (2022-09-26T15:49:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.