← Back to feed

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

L5 · ResearcherResearcharXiv· 4/15/2026

Directly addresses a fundamental HCI and generative AI challenge: how to design image generation systems that preserve user agency and creative intentionality rather than anchoring decisions prematurely.

AI Summary

Creo is a multi-stage text-to-image system that scaffolds image generation from rough sketches to high-resolution outputs, allowing users to maintain fine-grained control and creative agency throughout the process. The paper presents a comparative study showing that progressive generation with intermediate abstractions and decision-locking mechanisms increases user ownership, reduces output homogeneity, and improves controllability compared to one-shot generation.

Excerpt

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 mul

Read Original
0 upvotes · 0 downvotes · 1 min read

Related Articles

L5 · ResearcherResearcharXiv
Feature Information Dynamics in Diffusion

Researchers developed an information-theoretic framework to quantitatively measure when specific features are generated during diffusion model denoising, revealing representation differences across pixel, SDVAE, VAVAE, and RAE models.

L5 · ResearcherResearchLatent Space
[AINews] Quasi-Riemann-Hypothesis: OpenAI publishes 722 math papers solving 90 of the top 500 open math problems; “the most significant moment” in >100 years of mathematics

OpenAI published 722 math papers solving 90 top open problems using an internal AI model, including a breakthrough 'Quasi-Riemann Hypothesis' result.

L5 · ResearcherResearcharXiv
Optimal and Efficient Online Inverse Optimization

Researchers present a deterministic polynomial-time algorithm achieving optimal O(√d) regret for online inverse linear optimization, solving an open problem.

L5 · ResearcherResearcharXiv
Conformal Prediction Sets Quantify Information Gain: A Theoretical Perspective

Researchers establish theoretical foundations showing how conformal prediction set sizes quantify information gain, relating them to classical information-theoretic measures.

L5 · ResearcherResearcharXiv
QF3: Fast Flow RL with Filtered Q-Gradients

QF3 introduces a new off-policy reinforcement learning algorithm that trains flow policies 10x faster than previous methods for robot locomotion and manipulation tasks.