AnyDesign：通过无掩码扩散实现通用区域时尚编辑
AnyDesign: Versatile Area Fashion Editing via Mask-Free Diffusion
AI总结：
研究时尚图像编辑，扩展数据集涵盖更多服装和复杂背景，提出基于扩散的AnyDesign方法，通过融合服装类型和特征实现无掩码编辑，实验表明该方法优于当代文本引导时尚编辑方法。
摘要：
时尚图像编辑旨在根据给定指令修改人的外观。现有方法需辅助工具，缺乏灵活统一框架，且处理服装类型有限。本文扩展人类生成数据集，提出AnyDesign方法，可无掩码编辑通用区域，融合服装类型和特征，实验证明其性能优于当代方法。
英文摘要：
Fashion image editing aims to modify a person's appearance based on a given instruction. Existing methods require auxiliary tools like segmenters and keypoint extractors, lacking a flexible and unified framework. Moreover, these methods are limited in the variety of clothing types they can handle, as most datasets focus on people in clean backgrounds and only include generic garments such as tops, pants, and dresses. These limitations restrict their applicability in real-world scenarios. In this paper, we first extend an existing dataset for human generation to include a wider range of apparel and more complex backgrounds. This extended dataset features people wearing diverse items such as tops, pants, dresses, skirts, headwear, scarves, shoes, socks, and bags. Additionally, we propose AnyDesign, a diffusion-based method that enables mask-free editing on versatile areas. Users can simply input a human image along with a corresponding prompt in either text or image format. Our approach incorporates Fashion DiT, equipped with a Fashion-Guidance Attention (FGA) module designed to fuse explicit apparel types and CLIP-encoded apparel features. Both Qualitative and quantitative experiments demonstrate that our method delivers high-quality fashion editing and outperforms contemporary text-guided fashion editing methods.
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。
