With the rapid development of e-commerce, virtual try-on technology has become an essential tool to satisfy consumers' personalized clothing preferences. Diffusion-based virtual try-on systems aim to naturally align garments with target individuals, generating realistic and detailed try-on images. However, existing methods overlook the importance of garment size variations in meeting personalized consumer needs. To address this, we propose a novel virtual try-on method named SV-VTON, which introduces garment sizing concepts into virtual try-on tasks. The SV-VTON method first generates refined masks for multiple garment sizes, then integrates these masks with garment images at varying proportions, enabling virtual try-on simulations across different sizes. In addition, we developed a specialized size evaluation module to quantitatively assess the accuracy of size variations. This module calculates differences between generated size increments and international sizing standards, providing objective measurements of size accuracy. To further validate SV-VTON's generalization capability across different models, we conducted experiments on multiple SOTA Diffusion models. The results demonstrate that SV-VTON consistently achieves precise multi-size virtual try-on across various SOTA models, and validates the effectiveness and rationality of the proposed method, significantly fulfilling users' personalized multi-size virtual try-on requirements.

翻译：随着电子商务的飞速发展，虚拟试穿技术已成为满足消费者个性化服装需求的重要工具。基于扩散模型的虚拟试穿系统旨在将服装自然地适配于目标人体，生成逼真且细节丰富的试穿图像。然而，现有方法忽视了服装尺码变化在满足个性化消费者需求方面的重要性。为此，我们提出了一种名为SV-VTON的新型虚拟试穿方法，该方法将服装尺码概念引入虚拟试穿任务。SV-VTON方法首先生成多个服装尺码的精细化掩码，然后将这些掩码与不同比例的服装图像进行融合，从而实现跨不同尺码的虚拟试穿模拟。此外，我们开发了一个专门的尺码评估模块，用于定量评估尺码变化的准确性。该模块计算生成的尺码增量与国际尺码标准之间的差异，为尺码精度提供客观度量。为了进一步验证SV-VTON在不同模型间的泛化能力，我们在多个SOTA扩散模型上进行了实验。结果表明，SV-VTON在各种SOTA模型上均能实现精确的多尺码虚拟试穿，验证了所提方法的有效性与合理性，显著满足了用户个性化的多尺码虚拟试穿需求。