通过对话逐步优化图像生成，让模糊提示也能产出精准结果。
Twin Co-Adaptive Dialogue for Progressive Image Generation
- 用动态对话交互逐步修正图像，而非一次生成
- 减少用户试错次数，提升图像质量与意图契合度
- 适合需要精细调控的创意设计场景
现代文本到图像生成系统虽能产出高度逼真的视觉内容，但在处理用户提示中的固有歧义时仍表现不佳。本文提出Twin-Co框架，通过同步协同对话实现渐进式图像生成。系统首先根据用户提示生成基础图像，随后通过一系列同步对话交互，持续根据用户反馈调整优化图像。该协同适应机制能逐步消除歧义，更准确匹配用户意图。实验表明，Twin-Co不仅显著降低用户试错迭代次数，提升生成图像质量，还简化了各类应用场景下的创作流程。
原文摘要 · Abstract (English)
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 creative process across various applications.
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