MOFA-VTON：以细粒度适配实现虚拟试衣的更多时尚可能
MOFA-VTON: More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On
用用户手绘曲线草图构造双区域掩码替代 clothing-agnostic mask，并通过交叉注意力分上下半身独立学习布局对应关系，为可控人体图像生成提供了细粒度空间布局引导的可复用思路。
原文依据 we first design a mask construction strategy that transforms user-drawn curve sketches into a dual-region mask
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论虚拟试衣旨在将店铺中的服装图像贴合到特定人体上。最优的虚拟试衣方法应提供多样且灵活的穿着选项，准确反映现实场景中出现的各种穿着风格，并契合个人偏好与时尚诉求。然而，现有方法大多以相同的穿着模式用目标服装直接替换原有服装。这种对服装适配的有限控制可能导致试衣结果固定而单调。为探索虚拟试衣中细粒度适配带来的更多时尚可能，我们提出了一种新颖的虚拟试衣方法 MOFA-VTON，它允许用户通过简单草图调整试衣结果中的服装适配。具体而言，我们首先设计了一种掩码构建策略，将用户绘制的曲线草图转换为双区域掩码，替代传统的服装无关掩码，为后续生成过程提供细粒度的布局引导。进一步，我们提出了布局调整模块，利用交叉注意力机制分别学习人体上半身与下半身区域的布局对应关系，细化两个区域的空间排布。借助上述实现，我们的方法能够对目标服装进行灵活且细粒度的适配，克服固定布局的约束。在 VITON-HD 与 DressCode 数据集上的大量实验表明，我们提出的 MOFA-VTON 优于此前的最优方法，并为虚拟试衣提供了更多时尚可能。
英文原文摘要 1660 字符
Virtual try-on aims to fit an in-shop clothing image onto a specific human body. An optimal virtual try-on method should provide diverse and flexible dressing options, accurately reflecting the varied wearing styles encountered in real-life scenarios, tailored to individual preferences and fashion aspirations. However, current methods predominantly perform a direct replacement of the original clothing with the target clothing, following the same dressing pattern. This limited control over clothing adaptation may result in fixed and monotonous try-on outputs. To delve into More Fashion Possibilities with Fine-Grained Adaptations in Virtual Try-On, we propose a novel virtual try-on method, termed MOFA-VTON, which allows adjustment for clothing adaptations in try-on results through simple sketches by users. Specifically, we first design a mask construction strategy that transforms user-drawn curve sketches into a dual-region mask, replacing the traditional clothing-agnostic mask and providing fine-grained layout guidance for the subsequent generation process. Further, we propose layout adjustment blocks that utilize the cross-attention mechanism to independently learn layout correspondences for upper and lower regions of the human body, refining the spatial arrangement of the two regions. With these implementations, our method enables flexible and fine-grained adaptations of target clothing, overcoming the constraints of a fixed layout. Extensive experiments on VITON-HD and DressCode datasets demonstrate that our proposed MOFA-VTON outperforms previous state-of-the-art methods and provides more fashion possibilities for virtual try-on.