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.

Virtual Try-On (VTO) technologies are becoming a cornerstone of immersive retail experiences in the Metaverse. However, delivering real-time, high-fidelity interactions at scale remains a significant challenge, especially for Artificial Intelligence (AI) based applications that demand substantial computational resources. While edge servers effectively reduce latency and provide greater computational power than constrained User Equipment (UE), they face scalability issues under heavy multiuser loads.In this paper, we propose a novel scheme called Hybrid-Edge-VR4Fit. It is a hybrid offloading framework designed for scalable, low-latency VTO in 5G-edge environments. The system enhances responsiveness by leveraging edge inference to refine subsequent locally processed frames, reducing offloading frequency without compromising quality of service. We then introduce a dynamic offloading strategy modeled as a finite congestion game. Each UE autonomously adjusts its behavior based on real-time edge server and 5G network conditions to balance latency and quality trade-offs. Extensive experimental evaluations demonstrate that our proposal significantly reduces server congestion and queuing delays while maintaining visual fidelity, enabling responsive VTO services across diverse user densities.

Deep Learning architectures, albeit successful in most computer vision tasks, were designed for data with an underlying Euclidean structure, which is not usually fulfilled since pre-processed data may lie on a non-linear space. In this paper, we propose a geometry aware deep learning approach using rigid and non rigid transformation optimization for skeleton-based action recognition. Skeleton sequences are first modeled as trajectories on Kendall’s shape space and then mapped to the linear tangent space. The resulting structured data are then fed to a deep learning architecture, which includes a layer that optimizes over rigid and non rigid transformations of the 3D skeletons, followed by a CNN-LSTM network. The assessment on two large scale skeleton datasets, namely NTU-RGB+D and NTU-RGB+D 120, has proven that the proposed approach outperforms existing geometric deep learning methods and exceeds recently published approaches with respect to the majority of configurations.