MG-VTON: A Lightweight Virtual Try-on Model with Knowledge Distillation for Real-time Performance
Xuan Yu · Frontiers in Science and Engineering · 2025
In recent years, virtual try-on technology has seen a continuous surge in public visibility and has become a key tool for many companies to boost sales and enhance user experience. Existing virtual try-on methods are mainly divided into two categories: those based on Generative Adversarial Networks (GANs) and those based on diffusion models. GAN-based methods have been widely applied due to their compact model structures and fast execution speed, but there is still room for improvement in image quality and detail fidelity. In contrast, diffusion model-based methods excel in generating high-quality and realistic images, but their high computational complexity and slow inference speed limit their practicality in real-time applications. To address these issues, this paper proposes a lightweight and efficient virtual try-on model called MG-VTON that does not require human parsing. By introducing knowledge distillation techniques, we have streamlined the model to significantly improve computational efficiency and inference speed. Moreover, MG-VTON can still generate high-quality and realistic try-on effects without relying on human parsing. This work offers new insights for the further development of virtual try-on technology, enhancing the user experience and providing companies with more competitive solutions in digital apparel presentation and marketing.