DesignFromX: Empowering Consumer-Driven Design Space Exploration through Feature Composition of Referenced Products

2025-05-16

AI Summary

Non-expert users struggle to articulate design preferences due to unfamiliarity with technical terminology and limited understanding of design structure. Method: We propose the first visual-driven design paradigm for lay users, enabling design feature disentanglement, composition, and generation via image references. Our approach integrates multimodal large models (CLIP, Stable Diffusion, 3D diffusion), region-level image feature disentanglement, cross-modal prompt engineering, and an interactive feature orchestration interface to map ambiguous semantic inputs into structured concept graphs and editable 3D models. Contribution/Results: A user study (n=24) demonstrates that the system significantly lowers design barriers and user frustration while improving engagement and satisfaction. Notably, 92% of participants successfully generated concept prototypes aligned with their preferences—validating the paradigm’s effectiveness in reducing domain expertise dependency and enhancing the feasibility of user-centered design.

Technology Category

• Humans and AI: User Experience and Usability
• Computer Vision: Multi-modal Vision
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Industrial products are designed to satisfy the needs of consumers. The rise of generative artificial intelligence (GenAI) enables consumers to easily modify a product by prompting a generative model, opening up opportunities to incorporate consumers in exploring the product design space. However, consumers often struggle to articulate their preferred product features due to their unfamiliarity with terminology and their limited understanding of the structure of product features. We present DesignFromX, a system that empowers consumer-driven design space exploration by helping consumers to design a product based on their preferences. Leveraging an effective GenAI-based framework, the system allows users to easily identify design features from product images and compose those features to generate conceptual images and 3D models of a new product. A user study with 24 participants demonstrates that DesignFromX lowers the barriers and frustration for consumer-driven design space explorations by enhancing both engagement and enjoyment for the participants.

Problem

• Helps consumers articulate product design preferences effectively
• Enables feature composition from referenced products using GenAI
• Reduces barriers in consumer-driven design space exploration

Innovation

• GenAI-based framework for consumer-driven design
• Feature composition from referenced product images
• Generates conceptual images and 3D models