Image-based virtual try-on aims to synthesize a naturally dressed person image with a clothing image, which revolutionizes online shopping and inspires related topics within image generation, showing both research significance and commercial potential. However, there is a gap between current research progress and commercial applications and an absence of comprehensive overview of this field to accelerate the development. In this survey, we provide a comprehensive analysis of the state-of-the-art techniques and methodologies in aspects of pipeline architecture, person representation and key modules such as try-on indication, clothing warping and try-on stage. We additionally apply CLIP to assess the semantic alignment of try-on results, and evaluate representative methods with uniformly implemented evaluation metrics on the same dataset. In addition to quantitative and qualitative evaluation of current open-source methods, unresolved issues are highlighted and future research directions are prospected to identify key trends and inspire further exploration. The uniformly implemented evaluation metrics, dataset and collected methods will be made public available at https://github.com/little-misfit/Survey-Of-Virtual-Try-On.

基于图像的虚拟试戴旨在将穿着自然的人物图像与服装图像相结合，这彻底改变了在线购物并激发了图像生成中的相关主题，显示出研究意义和商业潜力。然而，目前的研究进展与商业应用之间存在差距，并且缺乏对该领域的全面概述以加速发展。在这项调查中，我们对管道架构、人物表现和关键模块（如试穿指示、服装翘曲和试穿舞台）等方面的最新技术和方法进行了全面分析。我们还应用 CLIP 来评估试穿结果的语义对齐，并在同一数据集上使用统一实施的评估指标评估代表性方法。除了对当前开源方法进行定量和定性评估外，还强调了未解决的问题，并展望了未来的研究方向，以确定关键趋势并激发进一步探索。统一实施的评估指标、数据集和收集的方法将在 https://github.com/little-misfit/Survey-Of-Virtual-Try-On 上公开。