AIdeation: Designing a Human-AI Collaborative Ideation System for Concept Designers

2025-02-20

AI Summary

Current AI tools primarily focus on text-to-image generation and lack support for early-stage conceptual design tasks—such as multi-source reference retrieval, cross-modal semantic-visual fusion exploration, and non-linear iterative ideation—critical to film, television, and game concept designers. To address this gap, we propose a novel human-AI co-creation paradigm tailored to conceptual design workflows. Our approach integrates cross-modal retrieval, image semantic parsing, interactive clustering, and interpretable visual recomposition to enable multi-round, hybrid-driven creative divergence. A user study (n=16) demonstrates statistically significant improvements in creativity, efficiency, and user satisfaction (p<.01). Further validation across four professional studios revealed that two have adopted the system in commercial production, confirming measurable gains in both design quality and workflow efficiency.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Computer Vision: Multi-modal Vision

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Concept designers in the entertainment industry create highly detailed, often imaginary environments for movies, games, and TV shows. Their early ideation phase requires intensive research, brainstorming, visual exploration, and combination of various design elements to form cohesive designs. However, existing AI tools focus on image generation from user specifications, lacking support for the unique needs and complexity of concept designers' workflows. Through a formative study with 12 professional designers, we captured their workflows and identified key requirements for AI-assisted ideation tools. Leveraging these insights, we developed AIdeation to support early ideation by brainstorming design concepts with flexible searching and recombination of reference images. A user study with 16 professional designers showed that AIdeation significantly enhanced creativity, ideation efficiency, and satisfaction (all p<.01) compared to current tools and workflows. A field study with 4 studios for 1 week provided insights into AIdeation's benefits and limitations in real-world projects. After the completion of the field study, two studios, covering films, television, and games, have continued to use AIdeation in their commercial projects to date, further validating AIdeation's improvement in ideation quality and efficiency.

Problem

• Human-AI collaborative ideation system
• Supporting concept designers' workflows
• Enhancing creativity and efficiency in design

Innovation

• Human-AI collaborative ideation system
• Flexible searching and recombination
• Enhanced creativity and efficiency