Integrating Virtual Reality (VR) and Human–Computer Interaction (HCI) has transformed user engagement with virtual environments, enhancing immersion and usability. Technologies like Cave Automatic Virtual Environment (CAVE) and Head-Mounted Displays (HMDs) have shown significant promise in visualizing data, especially for examining and comprehending intricate 3D datasets, such as graph visualizations. To explore the effectiveness of these technologies in data visualization, we conducted a user study comparing user experience and performance across these two systems when interacting with a large 3D graph. The virtual environment and interaction modalities were adapted to each platform: the HMD setup utilized dual 6-DOF controllers, while the CAVE configuration employed a Flystick2 controller and a trackball. Preliminary data on participants’ demographics, motion sickness sensitivity, and prior experience with graph theory were collected to provide context for the findings. Results show that users in the HMD condition reported significantly higher levels of perceived presence and involvement, as well as improved task performance in navigation and interaction tasks. While both systems were rated similarly for perceived usefulness and ease of use, the HMD environment offered a more immersive and emotionally positive experience overall. These findings contribute to immersive analytics research by demonstrating the comparative strengths of HMD-based systems for individual 3D graph exploration, while highlighting the potential advantages of CAVE for low-discomfort settings. The study underscores the importance of aligning system design with user profiles and task demands to optimize data exploration in virtual environments.

Generative AI techniques are revolutionizing the creation of 3D and immersive content, yet challenges remain, such as achieving precise user control in 3D generation. Current text-to-3D and image-to-3D pipelines often produce outputs that deviate from user expectations, lacking the ability to refine or correct generated models effectively. To address these limitations, we propose Imagin3D, a novel human-in-the-loop (HITL) system that integrates Multimodal Large Language Models to enhance the controllability and adaptability of 3D content generation. Imagin3D leverages a Multi-View Question Answering module to evaluate the consistency of generated views with user-provided textual descriptions, enabling iterative refinement through guided inpainting while preserving multi-view consistency. This allows users to co-create 3D models, which are then synthesized into a final 3D asset using Neural Rendering. We validate Imagin3D through extensive quantitative evaluations and a comprehensive user study, demonstrating its effectiveness in improving usability, accuracy, and user satisfaction in interactive 3D generation tasks. Our results highlight the potential of HITL approaches to bridge the gap between AI-generated outputs and user intent, paving the way for more accessible and user-centered 3D generation workflows.

The fashion industry is undergoing a digital transformation, driven by growing demands for sustainability, personalization and immersive experiences. In this paper, we present Made-In (Multimodal and Collaborative Artificial Intelligence for the Design of Inclusive and Sustainable Fashion): an immersive, human-in-the-loop analytics system designed to support fashion professionals in exploring, comparing and contextualizing product data across digital and social platforms. Unlike generative or simulation-based approaches, Made-In provides creative decision support by aggregating real-world data from luxury brand websites and social media. This enables designers and merchandisers to make informed, context-aware choices. The system comprises three core modules: a 3D configurator for visualizing product assortments; a collection grid interface for the comparative analysis of e-commerce data; and a social media trend detector based on deep learning pipelines for image classification, object detection and color clustering. Two curated datasets, one derived from Instagram and the other from fashion e-tailers, provide the system with analytics. A user study with domain experts confirms the platform’s usability and relevance for trend forecasting, sustainability evaluation and visual merchandising strategy. The results demonstrate that Made-In effectively bridges the gap between data analytics and human creativity in fashion, offering a scalable solution that aligns with EU goals for digital sustainability and inclusivity. • Immersive AI system that supports sustainable digital fashion exploration. • Real-time trend detection from Instagram enables geo-localized style insights. • Interactive 3D and collection grids enhance visual merchandising decisions. • AI modules extract product data, dominant colors, and sustainability tags. • Usability study confirms system effectiveness for designers and retailers.

Abstract In an age where consumer preferences are as diverse as they are dynamic, the ability to offer personalized fashion recommendations at scale remains a significant challenge for retailers. Consumers seek a shopping experience that not only understands their unique style preferences but also dynamically adapts to their evolving tastes. The fashion industry is at a crossroads, facing increasing consumer demand for personalization, sustainability and transparency in a rapidly evolving digital marketplace. Traditional retail practices, while rich in tradition and artistry, often struggle to up-to-date with the rapidly, ethically-conscious and technology-driven expectations of today’s consumers. “OutfitAI” is designed to address these challenges by leveraging the power of deep learning to revolutionize the fashion retail experience. By automating the process of background removal in fashion images, using advanced algorithms for personalized product matching, and integrating sustainability filters into the product discovery process, OutfitAI aims to deliver a shopping experience that is not only personalized and engaging, but also aligned with the ethical and environmental values of the contemporary consumer. Unlike existing solutions, OutfitAI uses state-of-the-art semantic segmentation for precise background removal, enabling detailed feature extraction from fashion images. This process enables accurate matching of user-uploaded images with similar fashion items from an extensive database of eco-friendly and ethically produced products sourced from leading e-tailers. Setting itself apart from the current state of the art, OutfitAI places a strong emphasis on ethical data use and privacy, implementing robust measures to ensure user privacy and transparency. It also pioneers the integration of sustainability into the digital fashion discovery process, promoting responsible consumption patterns among users. Through a comprehensive system architecture that combines technical innovation with a commitment to ethics and sustainability, OutfitAI not only addresses the technological needs of the fashion retail industry, but also responds to the growing demand for more responsible and transparent consumer technologies.

The convergence of Artificial Intelligence (AI), Computer Vision (CV), Computer Graphics (CG), and Extended Reality (XR) is driving innovation in immersive environments. A key challenge in these environments is the creation of personalized 3D assets, traditionally achieved through manual modeling, a time-consuming process that often fails to meet individual user needs. More recently, Generative AI (GenAI) has emerged as a promising solution for automated, context-aware content generation. In this paper, we present MineVRA (Multimodal generative artificial iNtelligence for contExt-aware Virtual Reality Assets), a novel Human-In-The-Loop (HITL) XR framework that integrates GenAI to facilitate coherent and adaptive 3D content generation in immersive scenarios. To evaluate the effectiveness of this approach, we conducted a comparative user study analyzing the performance and user satisfaction of GenAI-generated 3D objects compared to those generated by Sketchfab in different immersive contexts. The results suggest that GenAI can significantly complement traditional 3D asset libraries, with valuable design implications for the development of human-centered XR environments.