用任意设计图实现服装与配饰的自由编辑与试穿
FEAT: Fashion Editing and Try-On from Any Design
- 通过解耦注入技术，融合服装与非服装设计源
- 支持完整穿搭试穿，包括配饰且无残留衣物
- 无需训练即可实现精准试穿，适合创意设计场景
时尚设计旨在表达设计师的创作意图，并展现服饰与人体的互动关系。现有方法依赖多模态输入实现服饰编辑与虚拟试穿，但仍存在两大局限：（i）仅限于服饰相关图像，无法使用艺术作品、抽象图像或自然照片等创意设计源；（ii）难以支持包含配饰的完整穿搭。本文提出FEAT（Fashion Editing And Try-On from Any Design），可基于多样设计源实现服饰与配饰的编辑与虚拟试穿。为此，我们引入解耦双注入（DDI）机制，分别处理服装与非服装设计源，通过内容与风格解耦实现设计线索的精准注入。此外，提出正交引导噪声融合（OGNF），一种无需训练的机制，通过正交投影消除残留衣物，并采用区域特异性噪声策略，实现对服饰及配饰的虚拟试穿。大量实验表明，FEAT在设计灵活性、提示一致性与视觉真实感方面达到当前最优水平。
原文摘要 · Abstract (English)
Fashion design aims to express a designer's creative intent and to depict how garments interact with the human body. Recent methods condition on multimodal inputs to support garment editing and virtual try-on. However, existing methods still (i) confine design to garment-related images, excluding creative design sources such as artwork, abstract imagery, and natural photographs, and (ii) cannot support complete outfits, including accessories. We present FEAT (Fashion Editing And Try-On from Any Design), a method that enables editing and try-on across garments and accessories using diverse design sources. To achieve this, we introduce Disentangled Dual Injection (DDI). It takes both apparel and non-apparel design sources and selectively injects design cues via content and style disentanglement. Furthermore, we propose Orthogonal-Guided Noise Fusion (OGNF), a training-free mechanism that removes residual garments via orthogonal projection and applies region-specific noise strategies to enable virtual try-on for both garments and accessories. Extensive experiments demonstrate that FEAT achieves state-of-the-art performance in design flexibility, prompt consistency, and visual realism.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。