LLM时代下指令引导的图像与多媒体编辑控制：综述
Instruction-Guided Editing Controls for Images and Multimedia: A Survey in LLM era
AI总结：
本文综述了LLM时代下基于自然语言指令的图像与多媒体编辑技术，涵盖从GAN到扩散模型的方法及多模态集成，并探讨了时尚、3D和视频合成等应用，旨在降低视觉编辑门槛并推动该领域发展。
摘要：
大型语言模型（LLMs）与多模态学习的快速发展已经改变了数字内容创作与处理的方式。传统的视觉编辑工具需要大量专业知识，限制了可及性。近年来基于指令的编辑技术取得了长足进展，利用自然语言作为用户意图与复杂编辑操作之间的桥梁，实现了对视觉内容的直观交互。本综述对这些技术进行了概览，重点关注LLMs与多模态模型如何赋能用户在没有深厚技术知识的情况下实现精确的视觉修改。通过综合超过100篇文献，我们探讨了从生成对抗网络到扩散模型的方法，并考察了用于细粒度内容控制的多模态集成。我们讨论了在时尚、3D场景操作和视频合成等领域的实际应用，强调了可及性的提升以及与人类直觉的一致性。本综述比较了现有文献，重点突出LLM赋能的编辑，并指出了关键挑战以推动进一步研究。我们旨在使强大的视觉编辑在从娱乐到教育等各个行业中实现普及。欢迎感兴趣的读者访问我们的代码仓库：https://github.com/tamlhp/awesome-instruction-editing。
英文摘要：
The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation. Traditional visual editing tools require significant expertise, limiting accessibility. Recent strides in instruction-based editing have enabled intuitive interaction with visual content, using natural language as a bridge between user intent and complex editing operations. This survey provides an overview of these techniques, focusing on how LLMs and multimodal models empower users to achieve precise visual modifications without deep technical knowledge. By synthesizing over 100 publications, we explore methods from generative adversarial networks to diffusion models, examining multimodal integration for fine-grained content control. We discuss practical applications across domains such as fashion, 3D scene manipulation, and video synthesis, highlighting increased accessibility and alignment with human intuition. Our survey compares existing literature, emphasizing LLM-empowered editing, and identifies key challenges to stimulate further research. We aim to democratize powerful visual editing across various industries, from entertainment to education. Interested readers are encouraged to access our repository at https://github.com/tamlhp/awesome-instruction-editing.
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。
