Review of Image-Based Virtual Try-on: From Deep Learning to Diffusion Models

Image-based virtual try-on, as an economical and convenient technology in the virtual try-on domain, aims to synthesize realistic fitting effects by combining model images with clothing images. It has significant applications in online shopping, fashion design, and animation. Recently, generative large models represented by diffusion models have driven new breakthroughs and transformations in the field due to their stronger generative capabilities compared to traditional deep learning methods. However, there is a lack of comprehensive analysis and overview of image-based virtual try-on in the era of large models. This paper summarizes the key techniques of image-based virtual try-on, categorizes mainstream methods into three baseline processes: data preprocessing, warping generation, and try-on result generation. It also analyzes the implementation methods used in representative literature, compares major process methods, and introduces commonly used datasets, evaluation metrics, and loss functions in image-based virtual try-on. Finally, the paper discusses the challenges and limitations of image-based virtual try-on in the context of large models, and outlines future development and improvement directions for relevant technologies. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.