In current virtual try-on tasks, only the effect of clothing worn on a person is depicted. In practical applications, users still need to select suitable clothing from a vast array of individual clothing items, but existing clothes may not be able to meet the needs of users. Additionally, some user groups may be uncertain about what clothing combinations suit them and require clothing selection recommendations. However, the retrieval-based recommendation methods cannot meet users' personalized needs, so we propose the Generative Fashion Matching-aware Virtual Try-on Framework(GMVT). We generate coordinated and stylistically diverse clothing for users using the Generative Matching Module. In order to effectively learn matching information, we leverage large-scale matching dataset, and transfer this acquired knowledge to the current virtual try-on domain. Furthermore, we utilize the Virtual Try-on Module to visualize the generated clothing on the user's body. To validate the effectiveness of our approach, we enlisted the expertise of fashion designers for a professional evaluation, assessing the rationality and diversity of the clothing combinations and conducting an evaluation matrix analysis. Our method significantly enhances the practicality of virtual try-on, offering users a wider range of clothing choices and an improved user experience.

翻译：在当前虚拟试穿任务中，仅能呈现衣物穿在人身上的效果。实际应用中，用户仍需从大量离散衣物中选择合适款式，但现有服装可能无法满足用户需求。此外，部分用户群体可能对自身适合的服装搭配存在不确定性，需要服装选择建议。然而，基于检索的推荐方法无法满足用户的个性化需求，因此我们提出了生成式时尚搭配感知虚拟试穿框架。通过生成式搭配模块，我们为用户生成协调且风格多样的服装。为有效学习搭配信息，我们利用大规模搭配数据集，并将所学知识迁移至当前虚拟试穿领域。进一步地，我们采用虚拟试穿模块将生成的服装可视化呈现于用户身体上。为验证方法有效性，我们邀请时尚设计专家进行专业评估，从服装组合的合理性与多样性维度进行评价，并开展评估矩阵分析。本方法显著提升了虚拟试穿的实用性，为用户提供了更丰富的服装选择与更优化的体验感受。