Assessing the quality of 3D reconstructed models remains a key challenge in multimedia applications, especially in the context of cultural heritage, where visual fidelity and perceptual realism are equally crucial. This study investigates how reconstruction parameters, as well as existing objective quality metrics, align with human perception. In addition, we analyze how perceived quality and user interaction are related. A dataset of 3D models was generated by varying the number of input images, mesh complexity, and texture resolution. Results from a subjective study show that texture resolution significantly affects perceived quality, whereas variations in number of images and mesh complexity have a limited impact. Furthermore, interaction behavior was found to vary with perceived quality, with participants spending more time and exploring larger viewing angles for models receiving higher scores. These findings highlight the need for perceptually grounded, interaction-aware evaluation methodologies and provide guidelines for future perceptual optimization of 3D reconstruction pipelines.

Integrating 5G technology with Virtual Reality creates an advanced human-machine interface applicable to various fields. With its high-speed, stable connection, 5G enables seamless real-time user communication. Virtual Reality enhances personalized interaction and allows continuous monitoring without complex additional equipment. This study presents a use case where a clinician, connected through a PC, interacts with a patient using a Virtual Reality headset during physiotherapy sessions. The goal is to showcase the potential of 5G and Virtual Reality in enhancing telerehabilitation. While initial tests focused on patients recovering from rotator cuff surgery, the system can be easily adapted to support a wide range of treatment and rehabilitation protocols.

Extended reality technologies are increasingly used in cultural heritage for preserving and accessing sites and artworks, where 3D model acquisition and rendering are key. Despite progress in reconstruction methodologies, a standardized approach to quality assessment is still missing. This study aims to evaluate objective quality metrics —both image-based and model-based, Full Reference and No Reference— applied to 3D models generated using the Structure from Motion algorithm. By varying parameters such as the number of images, number of triangles, and texture resolution, we examine the impact of these factors on metric outcomes, aiming to assess their reliability in cultural heritage applications.

This work aims to investigate the existence of a leading sensory cue in a mulsemedia Virtual Reality application involving three senses: vision, hearing, and touch. On the one hand this study can help in gaining insights into how the different senses contribute to mulsemedia applications in Virtual Reality. On the other, the identification of the leading cue could drive the optimization of Virtual Reality applications in terms of Quality of Experience, stimuli definition, and transmission requirements. In this paper, we present a mulsemedia experimental protocol for a subjective test aimed at identifying the material of a virtual object. We present the encountered challenges and describe and discuss the obtained results.

This demo presents an interactive communication system based on immersive media for analyzing the users’ Quality of Experience in collaborative tasks. The interaction between users is studied in an asymmetric scenario where a peer-to-peer communication has been set up between a PC and a Virtual Reality headset. Two application scenarios have been considered: a Block Building task and a Treasure Hunt game. The two users will cooperate to perform the two tasks. The goal is to study the relation between the type of transmitted information (i.e., audio and video or audio only) and the quality and quantity of interaction. During the demo, participants will have the opportunity to try one of the designed applications.

Augmented reality is one of the enabling technologies of the upcoming future. Its usage in working and learning scenarios may lead to a better quality of work and training by helping the operators during the most crucial stages of processes. Therefore, the automatic detection of stress during augmented reality experiences can be a valuable support to prevent consequences on people's health and foster the spreading of this technology. In this work, we present the design of a non-invasive stress assessment approach. The proposed system is based on the analysis of the head movements of people wearing a Head Mounted Display while performing stress-inducing tasks. First, we designed a subjective experiment consisting of two stress-related tests for data acquisition. Then, a statistical analysis of head movements has been performed to determine which features are representative of the presence of stress. Finally, a stress classifier based on a combination of Support Vector Machines has been designed and trained. The proposed approach achieved promising performances thus paving the way for further studies in this research direction.

The increasing advancement of Virtual and Augmented Reality technologies opens new research perspectives in various fields. While traditional multimedia content quality assessment has been extensively investigated, different issues need to be addressed for these novel technologies. In particular, evaluating the quality of experience must consider several aspects, involving, on the one hand, the quality of the displayed multimedia content and, on the other hand, human factors. Due to its inherent subjectivity, defining objective metrics for Quality of Experience is complex. This paper aims to frame the problem of objective Quality of Experience assessment and propose research directions to be pursued in this field.