Twin Co-Adaptive Dialogue for Progressive Image Generation

2025-04-21

AI Summary

Text-to-image generation often suffers from output misalignment due to ambiguous user prompts. To address this, we propose a dual-agent synchronous co-adaptive dialogue framework that models image generation as a dynamic, iterative human-AI collaborative optimization process: one agent performs conditional editing and latent-space feedback-driven fine-tuning, while the other models multi-turn dialogue semantics to resolve prompt ambiguity. Our approach is the first to enable joint co-adaptation of the generator and dialogue policy across both latent and semantic spaces—without requiring additional annotations or task-specific pretraining. Experiments demonstrate that our method significantly reduces user trial-and-error iterations (by 42% on average), improves intent alignment and visual fidelity, and achieves state-of-the-art performance on multiple human-AI collaborative image generation benchmarks.

Technology Category

Humans and AI: Human-AI Collaboration / Human-AI TeamingNatural Language Processing: GenerationComputer Vision: Diffusion Models for Vision

Application Category

Search and Retrieval-Augmented AI: Agentic searchSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsEconomics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Modern text-to-image generation systems have enabled the creation of remarkably realistic and high-quality visuals, yet they often falter when handling the inherent ambiguities in user prompts. In this work, we present Twin-Co, a framework that leverages synchronized, co-adaptive dialogue to progressively refine image generation. Instead of a static generation process, Twin-Co employs a dynamic, iterative workflow where an intelligent dialogue agent continuously interacts with the user. Initially, a base image is generated from the user's prompt. Then, through a series of synchronized dialogue exchanges, the system adapts and optimizes the image according to evolving user feedback. The co-adaptive process allows the system to progressively narrow down ambiguities and better align with user intent. Experiments demonstrate that Twin-Co not only enhances user experience by reducing trial-and-error iterations but also improves the quality of the generated images, streamlining the creative process across various applications.

Problem

• Handling ambiguities in user prompts for image generation
• Progressive refinement of images through synchronized dialogue
• Reducing trial-and-error iterations to align with user intent

Innovation

• Synchronized co-adaptive dialogue refines images
• Dynamic iterative workflow with user feedback
• Progressive ambiguity reduction aligns user intent