A Novel Multi-User Deep-Learning Offloading Scheme for Virtual Try-On in 5G-Edge Networks

Hamza Kchok, Ilhem Fajjari, Faten Chaieb, Nadjib Aitsaadi, Mourad Guéroui, Richard Li

Abstract: Virtual Try-On (VTO) technology, powered by Augmented Reality (AR) and Artificial Intelligence (AI), is reshaping online retail with immersive, real-time user experiences. However, the demand for high-quality, real-time VTO interactions introduces computational and latency challenges. Offloading computations to edge devices, enabled by 5 G networks, offers a promising solution to reduce end-to-end (E2E) latency and support multi-user environments. This paper proposes an optimized VTO platform tailored for edge deployment, featuring a novel knowledge distillation scheme for efficient 3D hand reconstruction. Our knowledge distillation approach reduces the model size by up to 70 % compared to the teacher model, achieving close visual accuracy in simpler tasks, such as flat hand reconstruction, ensuring it meets the quality standards of VTO applications. Experimental results demonstrate that our edge-optimized VTO architecture effectively balances quality and latency, supporting scalable, real-time interactions and advancing VTO technology for next-generation e-commerce applications.

External IDs:dblp:conf/icc/KchokFCAGL25a