Plot'n Polish：基于文本生成图像扩散模型的零样本故事可视化与解耦编辑
Plot'n Polish: Zero-shot Story Visualization and Disentangled Editing with Text-to-Image Diffusion Models
AI总结：
针对现有故事可视化方法难以兼顾编辑灵活性与多帧一致性的问题，提出零样本框架Plot'n Polish，可实现一致的故事生成与多细节层级的细粒度可视化控制。
摘要：
文本生成图像扩散模型已展现出在多个领域生成多样化、精细化视觉内容的强大能力，故事可视化正成为一项极具前景的应用。但随着其在现实创意领域的应用日益增多，如何提供更强的控制、优化能力，以及生成后以一致的方式修改图像的能力，成为一项重要挑战。现有方法往往难以在保持多帧画面视觉与叙事一致性的同时，灵活实现精细或粗粒度的编辑，导致创作者无法顺畅地创作和打磨自己的视觉故事。为解决这些问题，我们提出Plot'n Polish——一个零样本框架，可实现一致的故事生成，并能在不同细节层级上对故事可视化效果进行细粒度控制。
英文摘要：
Text-to-image diffusion models have demonstrated significant capabilities to generate diverse and detailed visuals in various domains, and story visualization is emerging as a particularly promising application. However, as their use in real-world creative domains increases, the need for providing enhanced control, refinement, and the ability to modify images post-generation in a consistent manner becomes an important challenge. Existing methods often lack the flexibility to apply fine or coarse edits while maintaining visual and narrative consistency across multiple frames, preventing creators from seamlessly crafting and refining their visual stories. To address these challenges, we introduce Plot'n Polish, a zero-shot framework that enables consistent story generation and provides fine-grained control over story visualizations at various levels of detail.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
