Creo: From One-Shot Image Generation to Progressive, Co-Creative Ideation

2026-04-15

AI Summary

Existing text-to-image systems often prematurely commit to fine-grained details, constraining early-stage creative exploration and causing uncontrolled alterations during editing, which undermines users’ sense of agency and creative ownership. To address this, this work proposes Creo, a multi-stage text-to-image generation framework that supports progressive human-AI co-creation. Creo introduces intermediate abstract representations to enable phased refinement—from sketch to high-resolution output—allowing users to lock in confirmed decisions at each stage and apply localized, differential updates to specific regions or attributes without triggering global re-rendering and associated semantic drift. Experimental results demonstrate that Creo significantly enhances users’ perceived ownership and output diversity, outperforming one-shot generation baselines in controllability, creative expressiveness, and result richness.

Technology Category

• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Computer Vision: Diffusion Models for Vision
• Humans and AI: Intelligent User Interfaces

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Text-to-image (T2I) systems enable rapid generation of high-fidelity imagery but are misaligned with how visual ideas develop. T2I systems generate outputs that make implicit visual decisions on behalf of the user, often introduce fine-grained details that can anchor users prematurely and limit their ability to keep options open early on, and cause unintended changes during editing that are difficult to correct and reduce users' sense of control. To address these concerns, we present Creo, a multi-stage T2I system that scaffolds image generation by progressing from rough sketches to high-resolution outputs, exposing intermediary abstractions where users can make incremental changes. Sketch-like abstractions invite user editing and allow users to keep design options open when ideas are still forming due to their provisional nature. Each stage in Creo can be modified with manual changes and AI-assisted operations, enabling fine-grained, step-wise control through a locking mechanism that preserves prior decisions so subsequent edits affect only specified regions or attributes. Users remain in the loop, making and verifying decisions across stages, while the system applies diffs instead of regenerating full images, reducing drift as fidelity increases. A comparative study with a one-shot baseline shows that participants felt stronger ownership over Creo outputs, as they were able to trace their decisions in building up the image. Furthermore, embedding-based analysis indicates that Creo outputs are less homogeneous than one-shot results. These findings suggest that multi-stage generation, combined with intermediate control and decision locking, is a key design principle for improving controllability, user agency, creativity, and output diversity in generative systems.

Problem

• text-to-image generation
• user control
• creative ideation
• image editing
• generative systems

Innovation

• multi-stage generation
• intermediate abstractions
• decision locking
• co-creative ideation
• diff-based editing